diff --git a/static/data/dataset_history.json b/static/data/dataset_history.json index 6afecb3..2eed636 100644 --- a/static/data/dataset_history.json +++ b/static/data/dataset_history.json @@ -1,6 +1,6 @@ { "_readme": "Hand-maintained going forward — do not regenerate. To add a snapshot, run `python3 scripts/append_dataset_snapshot.py` (counts the live static/data/{datasets,hf_datasets}.json the same way src/lib/datasets.ts computeDatasetStats does: top-level datasets only, iNatAg-mini's images excluded) or append to `points` by hand: { period: \"\", date: \"\", datasetCount: , imageCount: }.", - "generatedAt": "2026-09-30", + "generatedAt": "2026-10-01", "annotations": [ { "atPeriod": "May 30", @@ -217,8 +217,14 @@ { "period": "Sep 30", "date": "2026-09-30", - "datasetCount": 500, + "datasetCount": 491, "imageCount": 7097132 + }, + { + "period": "Oct 2", + "date": "2026-10-01", + "datasetCount": 499, + "imageCount": 7201467 } ] } diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index c923776..f6529af 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -16677,7 +16677,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/data6030032", - "citation": "Elmer, K., & Kalacska, M. (2021). *A High-Accuracy GNSS Dataset of Ground Truth Points Collected within Îles-de-Boucherville National Park, Quebec, Canada* (Version 2) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.4504921", + "citation": "Elmer, K., & Kalacska, M. (2021). *A High-Accuracy GNSS Dataset of Ground Truth Points Collected within Îles-de-Boucherville National Park, Quebec, Canada* (Version 2) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.4504921", "bibtex": "@article{elmer2021high,\n title={A High-Accuracy GNSS Dataset of Ground Truth Points Collected within Îles-de-Boucherville National Park, Quebec, Canada},\n author={Elmer, Kathryn and Kalacska, Margaret},\n journal={Data},\n volume={6},\n pages={32},\n year={2021},\n publisher={MDPI}\n}", "data_size_bytes": 8476128195, "num_files": 5, @@ -16896,7 +16896,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/data10120204", - "citation": "De Freitas, A., Gama, F. F., Augusto Lopes Magalhaes, I., & SANO, E. D. S. O. N. (2025). *Early-Season Field Reference Dataset of Croplands in a Consolidated Agricultural Frontier in the Brazilian Cerrado* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17612590", + "citation": "De Freitas, A., Gama, F. F., Augusto Lopes Magalhaes, I., & SANO, E. D. S. O. N. (2025). *Early-Season Field Reference Dataset of Croplands in a Consolidated Agricultural Frontier in the Brazilian Cerrado* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17612590", "bibtex": "@article{defreitas2025early,\n title={Early-Season Field Reference Dataset of Croplands in a Consolidated Agricultural Frontier in the Brazilian Cerrado},\n author={de Freitas, Ana Larissa Ribeiro and Gama, Fábio Furlan and Magalhães, Ivo Augusto Lopes and Sano, Edson Eyji},\n journal={Data},\n volume={10},\n pages={204},\n year={2025},\n publisher={MDPI}\n}", "data_size_bytes": 754748, "num_files": 5, @@ -16935,7 +16935,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/data11020035", - "citation": "Dimitrov, P., Roumenina, E., Jelev, G., Filchev, L., Gikov, A., Kamenova, I., Ilieva, I., Ganeva, D., Kercheva, M., Banov, M., Krasteva, V., Kolchakov, V., Dimitrov, E., & Miteva, N. (2025). *In-situ and UAV dataset with crop and soil parameters obtained from winter wheat fields* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17475741", + "citation": "Dimitrov, P., Roumenina, E., Jelev, G., Filchev, L., Gikov, A., Kamenova, I., Ilieva, I., Ganeva, D., Kercheva, M., Banov, M., Krasteva, V., Kolchakov, V., Dimitrov, E., & Miteva, N. (2025). *In-situ and UAV dataset with crop and soil parameters obtained from winter wheat fields* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17475741", "bibtex": "@article{dimitrov2026situ,\n title={In Situ Crop and Soil Data and UAV Imagery from Winter Wheat Fields in a Bulgarian Site},\n author={Dimitrov, Petar and Roumenina, Eugenia and Jelev, Georgi and Filchev, Lachezar and Gikov, Alexander and Kamenova, Ilina and Ilieva, Iliana and Ganeva, Dessislava and Kercheva, Milena and Banov, Martin and Krasteva, Veneta and Kolchakov, Viktor and Dimitrov, Emil and Miteva, Nevena},\n journal={Data},\n volume={11},\n pages={35},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 14184002126, "num_files": 38, @@ -16981,7 +16981,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/data11070153", - "citation": "Chwietczuk, P., Lipiński, S., & Chwietczuk, P. (2026). *LeafScans-Orchard dataset* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.20187965", + "citation": "Chwietczuk, P., Lipiński, S., & Chwietczuk, P. (2026). *LeafScans-Orchard dataset* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.20187965", "bibtex": "@article{chwietczuk2026leafscans,\n title={LeafScans-Orchard: A Multi-Year Open RGB Scan Dataset of Orchard Plant Leaves for Species and Cultivar Classification},\n author={Chwietczuk, Paweł and Lipiński, Seweryn and Chwietczuk, Paulina},\n journal={Data},\n volume={11},\n pages={153},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 37260947164, "num_files": 6, @@ -17003,31 +17003,40 @@ ], "lat_lon": [], "country": "Tanzania", - "environment": "field", + "environment": "lab", "real_or_synthetic": "real", "crop_types": [ "maize" ], "sensor_modality": "rgb", - "imaging_equipment": [], + "imaging_equipment": [ + "Samsung Galaxy A12", + "Samsung Galaxy A54" + ], "collection_period": "2024-07-05 to 2024-07-06", - "platform": "ground", + "platform": "fixed", "input_data_format": "external", "annotation_format": "boundingBox", - "classes": [], - "license": "cc-0", + "classes": [ + "healthy", + "unhealthy" + ], + "license": "cc0-1.0", "documentation": "https://doi.org/10.3390/data11070175", "citation": "Kiwoli, L. (2026). RGB Maize Kernels Image Dataset for grain quality screening: A use case- Detection of Aflatoxins and Fumonisins in Maize Using Computer Vision (Version V1) [dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/IWAUTS", "bibtex": "@article{kiwoli2026rgb,\n title={RGB Image Dataset of White Maize Kernels with Visible Quality Defects for Computer Vision-Based Assessment of Mycotoxin Contamination Risk and Grain Quality},\n author={Kiwoli, Liston and Nyambo, Devotha Godfrey and Mgawe, Bonny and Kassim, Neema and Ally, Mussa},\n journal={Data},\n volume={11},\n pages={175},\n year={2026},\n publisher={MDPI}\n}", "source": "doi", "data_link": "https://doi.org/10.7910/DVN/IWAUTS", "examples_image_url": "", - "hosted_in_original_form": true + "hosted_in_original_form": true, + "num_images": 5143, + "num_files": 20, + "data_size_bytes": 15901331732 }, { "name": "UAV-Hyp_phenotyping", "machine_learning_task": "object_detection", - "agricultural_task": "", + "agricultural_task": "crop_phenotyping", "location": [ "Quedlinburg, Germany" ], @@ -17038,13 +17047,18 @@ "crop_types": [ "Hypericum perforatum" ], - "sensor_modality": "rgb-d", - "imaging_equipment": [], - "collection_period": "", - "platform": "ground", + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Mavic 3 Enterprise" + ], + "collection_period": "26 June to 5 September (26 acquisition dates)", + "platform": "uav", "input_data_format": "external", "annotation_format": "boundingBox", - "classes": [], + "classes": [ + "plant", + "flower" + ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/data11080208", "citation": "Y. Zhang et al. (2026-07-13): UAV-Hyp_ UAV Dataset for Remote Sensing based Phenotying of Hypericum perforatum. DOI:10.5447/ipk/2026/6", @@ -17052,7 +17066,10 @@ "source": "doi", "data_link": "https://doi.org/10.5447/ipk/2026/6", "examples_image_url": "", - "hosted_in_original_form": true + "hosted_in_original_form": true, + "num_images": 12653, + "num_files": 8356, + "data_size_bytes": 38100000000 }, { "name": "LULC_change_Kef-Siliana", @@ -17073,10 +17090,12 @@ "environment": "field", "real_or_synthetic": "real", "crop_types": [], - "sensor_modality": "rgb", - "imaging_equipment": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2" + ], "collection_period": "2017-2022", - "platform": "", + "platform": "satellite", "input_data_format": "external", "annotation_format": "segmentationMask", "classes": [], @@ -17087,7 +17106,10 @@ "source": "doi", "data_link": "https://hdl.handle.net/20.500.11766.1/FK2/YUXPQY", "examples_image_url": "", - "hosted_in_original_form": true + "hosted_in_original_form": true, + "num_images": 1, + "num_files": 5, + "data_size_bytes": 14530163 }, { "name": "Brazil_forest_cover_classification", @@ -17101,21 +17123,31 @@ "environment": "field", "real_or_synthetic": "real", "crop_types": [], - "sensor_modality": "rgb", - "imaging_equipment": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Landsat 5 TM", + "Landsat 8 OLI" + ], "collection_period": "1989-2018", - "platform": "ground", + "platform": "satellite", "input_data_format": "external", "annotation_format": "classLabel", - "classes": [], + "classes": [ + "forest", + "non-forest", + "water" + ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/data4030114", - "citation": "Kalacska, M., Lucanus, O., Arroyo-Mora, J. P., & Sousa, L. (2019). Multi-temporal forest cover classification for the Xingu River basin, Brazil (Version 1). figshare. https://doi.org/10.6084/m9.figshare.8303669.v1", + "citation": "Kalacska, M., Lucanus, O., Arroyo-Mora, J. P., & Sousa, L. (2019). *Multi-temporal forest cover classification for the Xingu River basin, Brazil* (Version 1). figshare. https://doi.org/10.6084/m9.figshare.8303669.v1", "bibtex": "@article{kalacska2019new,\n title={A New Multi-Temporal Forest Cover Classification for the Xingu River Basin, Brazil},\n author={Kalacska, Margaret and Lucanus, Oliver and Sousa, Leandro and Arroyo-Mora, J. Pablo},\n journal={Data},\n volume={4},\n pages={114},\n year={2019},\n publisher={MDPI}\n}", "source": "doi", - "data_link": "Kalacska, M., Lucanus, O., Arroyo-Mora, J. P., & Sousa, L. (2019). Multi-temporal forest cover classification for the Xingu River basin, Brazil (Version 1). figshare. https://doi.org/10.6084/m9.figshare.8303669.v1", + "data_link": "https://doi.org/10.6084/m9.figshare.8303669.v1", "examples_image_url": "", - "hosted_in_original_form": true + "hosted_in_original_form": true, + "num_images": 4, + "num_files": 6, + "data_size_bytes": 195085779 }, { "name": "Austria_forest_inventory_pointclouds", @@ -17151,7 +17183,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/data5040103", - "citation": "Gollob, C., Ritter, T., & Nothdurft, A. (2020). *LAUT - Terrestrial and Personal laser scanner data from Austrian forest Inventory plots* (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.3698955", + "citation": "Gollob, C., Ritter, T., & Nothdurft, A. (2020). *LAUT - Terrestrial and Personal laser scanner data from Austrian forest Inventory plots* (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.3698955", "bibtex": "@article{gollob2020comparison,\n title={Comparison of 3D Point Clouds Obtained by Terrestrial Laser Scanning and Personal Laser Scanning on Forest Inventory Sample Plots},\n author={Gollob, Christoph and Ritter, Tim and Nothdurft, Arne},\n journal={Data},\n volume={5},\n pages={103},\n year={2020},\n publisher={MDPI}\n}", "data_size_bytes": 15367190849, "num_files": 8, @@ -17185,7 +17217,7 @@ "classes": [], "license": "", "documentation": "https://doi.org/10.3390/data8120185", - "citation": "Luisa Fernanda Gómez Ossa, German Sanchez Torres, & John W. Branch-Bedoya. (2023). *Tropical Andes Land Cover Dataset (TALANDCOVER)* (Version v1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.8219552", + "citation": "Luisa Fernanda Gómez Ossa, German Sanchez Torres, & John W. Branch-Bedoya. (2023). *Tropical Andes Land Cover Dataset (TALANDCOVER)* (Version v1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.8219552", "bibtex": "@article{gomezossa2023land,\n title={Land Cover Classification in the Antioquia Region of the Tropical Andes Using NICFI Satellite Data Program Imagery and Semantic Segmentation Techniques},\n author={Gomez-Ossa, Luisa F. and Sanchez-Torres, German and Branch-Bedoya, John W.},\n journal={Data},\n volume={8},\n pages={185},\n year={2023},\n publisher={MDPI}\n}", "data_size_bytes": 747784045, "num_files": 1, @@ -17309,7 +17341,7 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/data8050086", - "citation": "Strautiņa, S., Kalniņa, I., Kaufmane, E., Sudars, K., Namatēvs, I., Ņikuļins, A., Judvaitis, J., & Balašs, R. (2022). RaspberrySet: Dataset of Annotated Raspberry Images for Object Detection. *Zenodo*. https://doi.org/10.5281/ZENODO.7014728", + "citation": "Strautiņa, S., Kalniņa, I., Kaufmane, E., Sudars, K., Namatēvs, I., Ņikuļins, A., Judvaitis, J., & Balašs, R. (2022). RaspberrySet: Dataset of Annotated Raspberry Images for Object Detection. *Zenodo*. https://doi.org/10.5281/ZENODO.7014728", "bibtex": "", "zip_size_bytes": 1546023922, "source": "huggingface", @@ -17433,7 +17465,7 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1371/journal.pone.0349210", - "citation": "Chowdhury, S. H., & Arman, M. S. (2025). *Tea Leaf Disease Image Dataset for Automated Classification of Tea Garden Diseases* [Dataset]. Mendeley Data. https://doi.org/10.17632/3X42RBJ8YV.1", + "citation": "Chowdhury, S. H., & Arman, M. S. (2025). *Tea Leaf Disease Image Dataset for Automated Classification of Tea Garden Diseases* [Dataset]. Mendeley Data. https://doi.org/10.17632/3X42RBJ8YV.1", "bibtex": "@article{chowdhury2026curated,\n title={A curated dataset and lightweight deep learning framework for tea leaf disease classification},\n author={Chowdhury, Sakibul Hasan and Arman, Md Shohel and Siam, Masrafe Bin Hannan and Khan, Md Rayhan and Hasan, Afia and Fahim, Parvez Ahmed Moju},\n journal={PLOS One},\n volume={21},\n pages={e0349210},\n year={2026},\n publisher={Public Library of Science}\n}", "zip_size_bytes": 2916601676, "source": "huggingface", @@ -17625,7 +17657,7 @@ ], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1371/journal.pone.0354583", - "citation": "lv, xiang, & ChengLei, S. (2026). *Tea-Leaf Disease and Pest Detection Dataset (v1.0)* (Version 1) [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.32253357.V1", + "citation": "lv, xiang, & ChengLei, S. (2026). *Tea-Leaf Disease and Pest Detection Dataset (v1.0)* (Version 1) [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.32253357.V1", "bibtex": "@article{lyu2026spatial,\n title={Spatial-aware lightweight network for real-time tea disease detection: A coordinate attention-enhanced YOLOv8n approach with path-decoupling strategy},\n author={Lyu, Xiang and Yu, Yue and Song, ChengLei},\n journal={PLOS One},\n volume={21},\n pages={e0354583},\n year={2026},\n publisher={Public Library of Science}\n}", "zip_size_bytes": 385811551, "source": "huggingface", @@ -17737,7 +17769,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1371/journal.pone.0319268", - "citation": "Saha, G., Shahrin, F., Hasin Khan, F., Mohammad Meshkat, M., & Azad, A. K. M. A. M. (2025). *Data from: Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.28120901.V2", + "citation": "Saha, G., Shahrin, F., Hasin Khan, F., Mohammad Meshkat, M., & Azad, A. K. M. A. M. (2025). *Data from: Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.28120901.V2", "bibtex": "@article{saha2025smart,\n title={Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system},\n author={Saha, Gourab and Shahrin, Fariha and Khan, Farhan Hasin and Meshkat, Mashook Mohammad and Azad, AKM Abdul Malek},\n journal={PLOS ONE},\n volume={20},\n pages={e0319268},\n year={2025},\n publisher={Public Library of Science}\n}", "data_size_bytes": 769922636, "num_files": 45, @@ -17817,7 +17849,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.1371/journal.pone.0353966", - "citation": "Bueno Dueñas, M., & Loayza Loza, H. M. L. (2026). *Dataset of Sentinel 2 Image Time Series for Multi-annual Crop Type Classification in Perú* (Version V2) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19949051", + "citation": "Bueno Dueñas, M., & Loayza Loza, H. M. L. (2026). *Dataset of Sentinel 2 Image Time Series for Multi-annual Crop Type Classification in Perú* (Version V2) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19949051", "bibtex": "@article{bueno2026can,\n title={Can a history of crop rotations improve the prediction of soil organic carbon in the Andes? integrating machine learning multi-annual crop classification as a proxy of soil management},\n author={Bueno, Marcelo and Loayza, Hildo and Ninanya, Johan and Rinza, Javier and Briceño, Percy and Silva, Luis and Mestanza, Carlos and Otiniano, Ronal and Kreuze, Jan and Ramírez, David A.},\n journal={PLOS One},\n volume={21},\n pages={e0353966},\n year={2026},\n publisher={Public Library of Science}\n}", "data_size_bytes": 7196747600, "num_files": 3, @@ -17892,7 +17924,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18020209", - "citation": "Yang, L., Dong, J., Xu, C., Huang, J., Wang, Y., Ma, H., Chen, Z., Wang, L., & Zhang, J. (2025). *2022-2024 Dataset of 10-meter Resolution Rice Planting Distribution in the Middle and Lower Reaches of the Yangtze River, China* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.29336981.V2", + "citation": "Yang, L., Dong, J., Xu, C., Huang, J., Wang, Y., Ma, H., Chen, Z., Wang, L., & Zhang, J. (2025). *2022-2024 Dataset of 10-meter Resolution Rice Planting Distribution in the Middle and Lower Reaches of the Yangtze River, China* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.29336981.V2", "bibtex": "@article{yang2026sparse,\n title={From Sparse to Refined Samples: Iterative Enhancement-Based PDLCM for Multi-Annual 10 m Rice Mapping in the Middle-Lower Yangtze},\n author={Yang, Lingbo and Dong, Jiancong and Xu, Cong and Huang, Jingfeng and Wang, Yichen and Ma, Huiqin and Chen, Zhongxin and Wang, Limin and Zhang, Jingcheng},\n journal={Remote Sensing},\n volume={18},\n pages={209},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 1749156590, "num_files": 3, @@ -17965,7 +17997,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18050791", - "citation": "Machicao, J., Barbosa, A. A., O. Salles, L., Toledo, P., Corrêa, P., Oliveira, L., Collevatti, R., de Souza, E. B., & Ometto, J. (2025). *[DATASET] Remote sensing imagery of threatened Vereda wetlands in the Brazilian Midwest: a segmentation-oriented model as a backbone for domain-specific applications* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17803750", + "citation": "Machicao, J., Barbosa, A. A., O. Salles, L., Toledo, P., Corrêa, P., Oliveira, L., Collevatti, R., de Souza, E. B., & Ometto, J. (2025). *[DATASET] Remote sensing imagery of threatened Vereda wetlands in the Brazilian Midwest: a segmentation-oriented model as a backbone for domain-specific applications* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17803750", "bibtex": "@article{machicao2026mapping,\n title={Mapping of Threatened Vereda Wetlands in the Brazilian Midwest Using a Domain-Specific U-Net},\n author={Machicao, Jeaneth and Barbosa, Alexandre Augusto and Salles, Leandro O. and Toledo, Peter Mann and Corrêa, Pedro Luiz P. and Oliveira, Luiz Flamarion B. and Collevatti, Rosane Garcia and Barroso de Souza, Eduardo and Ometto, Jean Pierre H. B.},\n journal={Remote Sensing},\n volume={18},\n pages={791},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 2486533310, "num_files": 1, @@ -17996,7 +18028,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18060871", - "citation": "Jie Yang, & Xin Huang. (2026). *The 30 m annual land cover datasets and its dynamics in China from 1985 to 2025* (Version 1.0.5) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18180184", + "citation": "Jie Yang, & Xin Huang. (2026). *The 30 m annual land cover datasets and its dynamics in China from 1985 to 2025* (Version 1.0.5) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18180184", "bibtex": "@article{zhang2026process,\n title={A Process-Oriented Spatiotemporal Graph Framework for Analyzing Land-Cover Change from Remote Sensing Time Series},\n author={Zhang, Qian and Zou, Xinyu and Chen, Weiwen and Shi, Tong and Liu, Meiling and Liu, Xiangnan},\n journal={Remote Sensing},\n volume={18},\n pages={871},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 60566523891, "num_files": 76, @@ -18124,7 +18156,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18121899", - "citation": "Yoo, J., Qi, Y., Ashe-McNally, I., MacDonald, B., & Wilson, J. P. (2025). *Urban Tree Canopy Data for Deep Learning Applications in Boyle Heights and City Terrace, Los Angeles, California, USA* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17459767", + "citation": "Yoo, J., Qi, Y., Ashe-McNally, I., MacDonald, B., & Wilson, J. P. (2025). *Urban Tree Canopy Data for Deep Learning Applications in Boyle Heights and City Terrace, Los Angeles, California, USA* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17459767", "bibtex": "@article{yoo2026open,\n title={An Open and Transferable Deep Learning Framework for Mapping Urban Tree Canopy Using NAIP Imagery},\n author={Yoo, Jooyoung and Qi, Yi and Ashe-McNalley, Isaac and MacDonald, Beau and Wilson, John P.},\n journal={Remote Sensing},\n volume={18},\n pages={1899},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 637593685, "num_files": 1, @@ -18191,7 +18223,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18132215", - "citation": "Shahbandeh, M., Kaim, D., & Kozak, J. (2026). *Forest Mapping Dataset Based on Contemporary Global Land Cover Products in Poland* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.20816283", + "citation": "Shahbandeh, M., Kaim, D., & Kozak, J. (2026). *Forest Mapping Dataset Based on Contemporary Global Land Cover Products in Poland* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.20816283", "bibtex": "@article{shahbandeh2026contemporary,\n title={Using Contemporary Global Land Cover Products to Improve Forest Mapping at the National Scale: Case Study of Poland},\n author={Shahbandeh, Mahsa and Kaim, Dominik and Kozak, Jacek},\n journal={Remote Sensing},\n volume={18},\n pages={2215},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 845581020, "num_files": 1, @@ -18233,7 +18265,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18152471", - "citation": "Alonso, L., Ana, S.-C., Fernando, C., Picos, J., & Armesto, J. (2025). *LiDAR pointcloud (MLS) for tree species classification* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17035446", + "citation": "Alonso, L., Ana, S.-C., Fernando, C., Picos, J., & Armesto, J. (2025). *LiDAR pointcloud (MLS) for tree species classification* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17035446", "bibtex": "@article{alonso2026automated,\n title={Automated Individual-Tree Species Recognition Using Mobile Laser Scanner Point Clouds, Random Forest and 2D Convolutional Neural Networks},\n author={Alonso, Laura and Solares-Canal, Ana and Costas, Fernando and Picos, Juan and Armesto, Julia},\n journal={Remote Sensing},\n volume={18},\n pages={2471},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 4659783475, "num_files": 12, @@ -18266,7 +18298,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18162830", - "citation": "Machidon, A., & Naparus-Aljancic, M. (2026). Evaluating Seasonal Fidelity and Cross-Site Structural Discrimination of Sentinel-2 LAI Products in Karst Forests. *Zenodo*. https://doi.org/10.5281/ZENODO.21899851", + "citation": "Machidon, A., & Naparus-Aljancic, M. (2026). Evaluating Seasonal Fidelity and Cross-Site Structural Discrimination of Sentinel-2 LAI Products in Karst Forests. *Zenodo*. https://doi.org/10.5281/ZENODO.21899851", "bibtex": "@article{nprualjani2026evaluating,\n title={Evaluating Seasonal Fidelity and Cross-Site Structural Discrimination of Sentinel-2 LAI Products in Karst Forests},\n author={Năpăruş-Aljančič, Magdalena and Machidon, Alina L. and Vilhar, Urša and Kozamernik, Erika and Kutnar, Lado and Kermavnar, Janez and Kafol, Žan and Ravbar, Nataša and Pipan, Tanja},\n journal={Remote Sensing},\n volume={18},\n pages={2830},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 1350110831, "num_files": 12, @@ -18299,7 +18331,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18172857", - "citation": "Florea, C., & Dinescu, A.-A. (2026). *PixelSet-Slovakia a Multi-Year Sentinel-2 Dataset from Slovakia* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.21885996", + "citation": "Florea, C., & Dinescu, A.-A. (2026). *PixelSet-Slovakia a Multi-Year Sentinel-2 Dataset from Slovakia* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.21885996", "bibtex": "@article{dinescu2026cross,\n title={Cross-Domain Mixup for Parcel-Level Crop Mapping on a Multi-Year Sentinel-2 Dataset from Slovakia},\n author={Dinescu, Antonela-Adelina and Florea, Corneliu},\n journal={Remote Sensing},\n volume={18},\n pages={2857},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 31251673271, "num_files": 1, @@ -18337,7 +18369,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18183118", - "citation": "Borgo, A., Thierion, V., Satta, G. G. A., Trabucco, A., Lupia, F., & Debolini, M. (2026). *High-resolution crop map of Sardinia region applying IOTA2 chain, 2018* [Graphic]. Zenodo. https://doi.org/10.5281/ZENODO.21276837", + "citation": "Borgo, A., Thierion, V., Satta, G. G. A., Trabucco, A., Lupia, F., & Debolini, M. (2026). *High-resolution crop map of Sardinia region applying IOTA2 chain, 2018* [Graphic]. Zenodo. https://doi.org/10.5281/ZENODO.21276837", "bibtex": "@article{borgo2026applying,\n title={Applying the IOTA2 Chain for Automated 10 m Crop Map Production in a Mediterranean Environment},\n author={Borgo, Andrea and Thierion, Vincent and Satta, Gabriele Giuseppe Antonio and Trabucco, Antonio and Lupia, Flavio and Marras, Serena and Debolini, Marta},\n journal={Remote Sensing},\n volume={18},\n pages={3118},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 1014918606, "num_files": 2, @@ -18372,7 +18404,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18183241", - "citation": "Wang, J., Xue, B., Liu, Y., Lu, T., & Shi, Y. (2026). *OCQT-PTS: A Multi-Region Rice Phenology Time-Series Dataset* (Version 1.1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.21723694", + "citation": "Wang, J., Xue, B., Liu, Y., Lu, T., & Shi, Y. (2026). *OCQT-PTS: A Multi-Region Rice Phenology Time-Series Dataset* (Version 1.1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.21723694", "bibtex": "@article{wang2026cross,\n title={Cross-Regional Classification of Rice Cropping Systems Based on Within-Year Seasonal Composition Using Multimodal Remote-Sensing Time Series},\n author={Wang, Jingrou and Lu, Tingting and Xue, Beibei and Xu, Lu and Shi, Yanyan and Ming, Dongping},\n journal={Remote Sensing},\n volume={18},\n pages={3241},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 1517696380, "num_files": 2, @@ -18434,7 +18466,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18183262", - "citation": "Cavalli, R. M., Esposito, G., Pisano, L., & Notti, D. (2026). *Supplemetnary data and code of \"Surface soil moisture from Sentinel-2 imagery: a systematic review complemented by a case study in Sardinia, Italy\"* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.22142621", + "citation": "Cavalli, R. M., Esposito, G., Pisano, L., & Notti, D. (2026). *Supplemetnary data and code of \"Surface soil moisture from Sentinel-2 imagery: a systematic review complemented by a case study in Sardinia, Italy\"* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.22142621", "bibtex": "@article{cavalli2026surface,\n title={Surface Soil Moisture from Sentinel-2 Imagery: A Systematic Review Complemented by a Case Study in Sardinia, Italy},\n author={Cavalli, Rosa Maria and Esposito, Giuseppe and Pisano, Luca and Notti, Davide},\n journal={Remote Sensing},\n volume={18},\n pages={3262},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 13799704, "num_files": 9, @@ -18498,7 +18530,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18101448", - "citation": "ESPEJO VALERO, O. S. C. A. R. J. A. V. I. E. R., ZARAZA, M. A. Y. C. O. L., BASTIDAS, K. A. R. E. N., PERILLA, A. R. I. E. L., ZAMBRANO, N. A. T. A. L. I. A., SANDOVAL, J., JUAN, R. O. D. R. I. G. U. E. Z., MAYORGA, C. R. I. S. T. I. N. A., CASAS, O. S. C. A. R., & JAIME, O. (2026). Artificial Intelligence and Cloud Computing for a New generation of Corine Land Cover Maps in Colombia. *Zenodo*. https://doi.org/10.5281/ZENODO.19924210", + "citation": "ESPEJO VALERO, O. S. C. A. R. J. A. V. I. E. R., ZARAZA, M. A. Y. C. O. L., BASTIDAS, K. A. R. E. N., PERILLA, A. R. I. E. L., ZAMBRANO, N. A. T. A. L. I. A., SANDOVAL, J., JUAN, R. O. D. R. I. G. U. E. Z., MAYORGA, C. R. I. S. T. I. N. A., CASAS, O. S. C. A. R., & JAIME, O. (2026). Artificial Intelligence and Cloud Computing for a New generation of Corine Land Cover Maps in Colombia. *Zenodo*. https://doi.org/10.5281/ZENODO.19924210", "bibtex": "@article{espejo2026artificial,\n title={Artificial Intelligence and Cloud Computing for a New Generation of Corine Land Cover Maps in Colombia},\n author={Espejo, Javier and Zaraza, Maycol and Bastidas, Karen and Perilla, Ariel and Zambrano, Natalia and Sandoval, Jonathan and Rodríguez, Juan and Mayorga, Cristina and Ramírez, Diana and Casas, Oscar and Sanclemente, Xiomara and Morales, Silvia and Orejarena, Jaime},\n journal={Remote Sensing},\n volume={18},\n pages={1448},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 13149621, "num_files": 3, @@ -18531,7 +18563,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18132181", - "citation": "Petsoulas, C., Evangelou, E., Tsitouras, A., Vlachostergios, D., Kargiotidou, A., & Pankou, C. (2026). *UAV multispectral phenotyping data of an M₂ sesame (Sesamum indicum L.) mutagenesis population under contrasting irrigation regimes (Larissa, Greece, 2025)* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19923837", + "citation": "Petsoulas, C., Evangelou, E., Tsitouras, A., Vlachostergios, D., Kargiotidou, A., & Pankou, C. (2026). *UAV multispectral phenotyping data of an M₂ sesame (Sesamum indicum L.) mutagenesis population under contrasting irrigation regimes (Larissa, Greece, 2025)* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19923837", "bibtex": "@article{petsoulas2026uav,\n title={UAV Remote Sensing for Drought-Adaptive Sesame Breeding: Flight-Altitude Benchmarking, Predictive Modelling, and Composite Stress Tolerance Indexing},\n author={Petsoulas, Christos and Tsitouras, Alexandros and Evangelou, Eleftherios and Kargiotidou, Anastasia and Pankou, Chrysanthi I. and Vlachostergios, Dimitrios N.},\n journal={Remote Sensing},\n volume={18},\n pages={2181},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 436780, "num_files": 1, @@ -18572,53 +18604,14 @@ "documentation": "https://doi.org/10.3390/agriculture16010032", "citation": "", "bibtex": "@article{li2026novel,\n title={A Novel Dataset Generation Strategy and a Multi-Period Farmland Cultivation Zones Dataset from Unmanned Aerial Vehicle Imagery},\n author={Li, Zirui and Gu, Jinping and Shang, Siying and Zhou, Yang and Luo, Qing and Zheng, Mingxue and Li, Xiaokai and Lin, Chengjun and Guan, Xuefeng},\n journal={Agriculture},\n volume={16},\n pages={32},\n year={2026},\n publisher={MDPI}\n}", - "data_size_bytes": 28919, - "num_files": 4, - "num_images": 0, + "data_size_bytes": 27178819129, + "num_files": 12937, + "num_images": 6467, "source": "doi", "data_link": "https://huggingface.co/datasets/a442823027/MPFCZ", "examples_image_url": "", "hosted_in_original_form": true }, - { - "name": "fruit_trees_apple_cherry_classification", - "machine_learning_task": "unlabeled", - "agricultural_task": "crop_segmentation", - "location": [ - "Shunyi District, Beijing, China" - ], - "lat_lon": [ - "40.35, 116.9" - ], - "country": "China", - "environment": "field", - "real_or_synthetic": "real", - "crop_types": [ - "apple", - "cherry" - ], - "sensor_modality": "rgb-d", - "imaging_equipment": [ - "Trimble TX8", - "Class 1 invisible laser" - ], - "collection_period": "9 March 2024", - "platform": "ground", - "input_data_format": "external", - "annotation_format": "none", - "classes": [], - "license": "mit", - "documentation": "https://doi.org/10.3390/agriculture15141463", - "citation": "", - "bibtex": "@article{cao2025novel,\n title={A Novel Adaptive Cuboid Regional Growth Algorithm for Trunk–Branch Segmentation of Point Clouds from Two Fruit Tree Species},\n author={Cao, Yuheng and Wang, Ning and Wu, Bin and Zhang, Xin and Wang, Yaxiong and Xu, Shuting and Zhang, Man and Miao, Yanlong and Kang, Feng},\n journal={Agriculture},\n volume={15},\n pages={1463},\n year={2025},\n publisher={MDPI}\n}", - "data_size_bytes": 32847263, - "num_files": 8, - "num_images": 0, - "source": "doi", - "data_link": "https://github.com/henry3539/ACRGS", - "examples_image_url": "", - "hosted_in_original_form": true - }, { "name": "TeaPickingNet_behavior_classification", "machine_learning_task": "unlabeled", @@ -18646,9 +18639,9 @@ "documentation": "https://doi.org/10.3390/agriculture15232441", "citation": "", "bibtex": "@article{han2025teapickingnet,\n title={TeaPickingNet: Towards Robust Recognition of Fine-Grained Picking Actions in Tea Gardens Using an Attention-Enhanced Framework},\n author={Han, Ru and Zheng, Ye and Shu, Lei and Cielniak, Grzegorz},\n journal={Agriculture},\n volume={15},\n pages={2441},\n year={2025},\n publisher={MDPI}\n}", - "data_size_bytes": 2412964, - "num_files": 79, - "num_images": 7, + "data_size_bytes": null, + "num_files": null, + "num_images": 12195, "source": "doi", "data_link": "https://doi.org/10.21227/dnkh-8e73", "examples_image_url": "", @@ -18686,89 +18679,14 @@ "documentation": "https://doi.org/10.3390/agriculture15232518", "citation": "", "bibtex": "@article{long2025hieraedgenet,\n title={HieraEdgeNet: A Multi-Scale Edge-Enhanced Framework for Automated Pollen Recognition},\n author={Long, Yuchong and Sun, Wen and Sun, Ningxiao and Wang, Wenxiao and Li, Chao and Yin, Shan},\n journal={Agriculture},\n volume={15},\n pages={2518},\n year={2025},\n publisher={MDPI}\n}", - "data_size_bytes": 17004570, - "num_files": 958, - "num_images": 5, + "data_size_bytes": 88080860468, + "num_files": null, + "num_images": 44471, "source": "doi", "data_link": "https://www.kaggle.com/datasets/ayinven/hieraedgenetintegratesdatasets", "examples_image_url": "", "hosted_in_original_form": true }, - { - "name": "raspberry_color_classification", - "machine_learning_task": "unlabeled", - "agricultural_task": "crop_detection", - "location": [ - "eastern Poland, Poland" - ], - "lat_lon": [ - "51.194325, 21.816613" - ], - "country": "Poland", - "environment": "field", - "real_or_synthetic": "real", - "crop_types": [ - "raspberry" - ], - "sensor_modality": "rgb", - "imaging_equipment": [ - "Samsung Galaxy S25 smartphone (Samsung Electronics Co., Ltd., Suwon-si, Republic of Korea) equipped with RGB main camera (50 MP, 8160 × 6120 px F/1.8 aperture, sensor size 1/1.56 inches and 24 mm lens)", - "Hohem iSteady M7 gimbal (Shenzhen Hohem Technology Co., Ltd., Shenzhen Guangdong, China)" - ], - "collection_period": "20 June and 20 September 2025", - "platform": "smartphone", - "input_data_format": "external", - "annotation_format": "none", - "classes": [], - "license": "mit", - "documentation": "https://doi.org/10.3390/agriculture15242530", - "citation": "", - "bibtex": "@article{buczyski2025detection,\n title={Detection of Red, Yellow, and Purple Raspberry Fruits Using YOLO Models},\n author={Buczyński, Kamil and Kapłan, Magdalena and Jarosz, Zbigniew},\n journal={Agriculture},\n volume={15},\n pages={2530},\n year={2025},\n publisher={MDPI}\n}", - "data_size_bytes": 339283, - "num_files": 14, - "num_images": 3, - "source": "doi", - "data_link": "https://github.com/kamilczynski/Detection-of-Red-Yellow-and-Purple-Raspberry-Fruits-Using-YOLO-Models", - "examples_image_url": "", - "hosted_in_original_form": true - }, - { - "name": "apple_chip_budding_site_classification", - "machine_learning_task": "unlabeled", - "agricultural_task": "crop_detection", - "location": [ - "eastern Poland, Poland" - ], - "lat_lon": [ - "51.224726, 22.426559" - ], - "country": "Poland", - "environment": "field", - "real_or_synthetic": "real", - "crop_types": [ - "apple" - ], - "sensor_modality": "rgb", - "imaging_equipment": [ - "Samsung Galaxy S25 smartphone (Samsung Electronics Co., Ltd., Suwon-si, Republic of Korea) equipped with an RGB camera sensor (1/1.56″, 50 MP)" - ], - "collection_period": "2025 growing season", - "platform": "smartphone", - "input_data_format": "external", - "annotation_format": "none", - "classes": [], - "license": "mit", - "documentation": "https://doi.org/10.3390/agriculture15242565", - "citation": "", - "bibtex": "@article{kapan2025detection,\n title={Detection and Segmentation of Chip Budding Graft Sites in Apple Nursery Using YOLO Models},\n author={Kapłan, Magdalena and Wójcik, Damian I. and Buczyński, Kamil},\n journal={Agriculture},\n volume={15},\n pages={2565},\n year={2025},\n publisher={MDPI}\n}", - "data_size_bytes": 9951570, - "num_files": 20, - "num_images": 6, - "source": "doi", - "data_link": "https://github.com/kamilczynski/Detection-of-Chip-Budding-Graft-Sites-in-Apple-Nursery-Using-YOLO-Models", - "examples_image_url": "", - "hosted_in_original_form": true - }, { "name": "WSP_spray_quality_classification", "machine_learning_task": "unlabeled", @@ -18873,9 +18791,9 @@ "documentation": "https://doi.org/10.3390/agronomy15061396", "citation": "", "bibtex": "@article{guo2025frpnet,\n title={FRPNet: A Lightweight Multi-Altitude Field Rice Panicle Detection and Counting Network Based on Unmanned Aerial Vehicle Images},\n author={Guo, Yuheng and Zhan, Wei and Zhang, Zhiliang and Zhang, Yu and Guo, Hongshen},\n journal={Agronomy},\n volume={15},\n pages={1396},\n year={2025},\n publisher={MDPI}\n}", - "data_size_bytes": 604343, - "num_files": 97, - "num_images": 0, + "data_size_bytes": 403838490, + "num_files": 1, + "num_images": 5372, "source": "doi", "data_link": "https://github.com/changcaiyang/Panicle-AI/releases/tag/v1.0", "examples_image_url": "", @@ -18919,43 +18837,6 @@ "examples_image_url": "", "hosted_in_original_form": true }, - { - "name": "corn_row_navigation_root_classification", - "machine_learning_task": "unlabeled", - "agricultural_task": "crop_detection", - "location": [ - "Changchun, Jilin Province, China" - ], - "lat_lon": [ - "43.81, 125.41" - ], - "country": "China", - "environment": "field", - "real_or_synthetic": "real", - "crop_types": [ - "corn" - ], - "sensor_modality": "rgb", - "imaging_equipment": [ - "Redmi Note 11 Pro; Xiaomi Inc., Beijing, China" - ], - "collection_period": "2024", - "platform": "smartphone", - "input_data_format": "external", - "annotation_format": "none", - "classes": [], - "license": "other", - "documentation": "https://doi.org/10.3390/agronomy15092069", - "citation": "", - "bibtex": "@article{xu2025farmland,\n title={Farmland Navigation Line Extraction Method Based on RS-LineNet Network and Root Subordination Relationship Optimization},\n author={Xu, Yanlei and Lu, Zhen and Li, Jian and Zhai, Yuting and Liu, Chao and Zhang, Xinyu and Zhou, Yang},\n journal={Agronomy},\n volume={15},\n pages={2069},\n year={2025},\n publisher={MDPI}\n}", - "data_size_bytes": 8655, - "num_files": 5, - "num_images": 0, - "source": "doi", - "data_link": "https://github.com/Zhenrrrrr/RS-LineNet", - "examples_image_url": "", - "hosted_in_original_form": true - }, { "name": "soybeanweed_classification", "machine_learning_task": "unlabeled", @@ -19103,9 +18984,9 @@ "documentation": "https://doi.org/10.1371/journal.pone.0317739", "citation": "", "bibtex": "@article{ferreiradasilva2025ai,\n title={AI enabled, mobile soil pH classification with colorimetric paper sensors for sustainable agriculture},\n author={Ferreira da Silva, Ademir and Ohta, Ricardo Luis and Tirapu Azpiroz, Jaione and Esteves Ferreira, Matheus and Marçal, Daniel Vitor and Botelho, André and Coppola, Tulio and Melo de Oliveira, Allysson Flavio and Bettarello, Murilo and Schneider, Lauren and Vilaça, Rodrigo and Abdool, Noorunisha and Junior, Vanderlei and Furlaneti, Wellington and Malanga, Pedro Augusto and Steiner, Mathias},\n journal={PLOS ONE},\n volume={20},\n pages={e0317739},\n year={2025},\n publisher={Public Library of Science}\n}", - "data_size_bytes": 14969869, - "num_files": 18, - "num_images": 1, + "data_size_bytes": 3312544480, + "num_files": 7, + "num_images": null, "source": "doi", "data_link": "https://doi.org/10.24435/materialscloud:vt-4t", "examples_image_url": "", @@ -19141,9 +19022,9 @@ "documentation": "https://doi.org/10.1371/journal.pone.0332165", "citation": "", "bibtex": "@article{amoah2025floralarea,\n title={FloralArea: AI-powered algorithm for automated calculation of floral area from flower images to support plant and pollinator research},\n author={Amoah, Edward I. and White, Khayri and Patch, Harland M. and Grozinger, Christina M.},\n journal={PLOS One},\n volume={20},\n pages={e0332165},\n year={2025},\n publisher={Public Library of Science}\n}", - "data_size_bytes": 114748026, - "num_files": 18, - "num_images": 1, + "data_size_bytes": 490504390, + "num_files": 5, + "num_images": null, "source": "doi", "data_link": "https://scholarsphere.psu.edu/resources/33452dff-b807-44b0-8783-71c8c47b5242", "examples_image_url": "", @@ -19181,7 +19062,7 @@ "bibtex": "@article{aghamohammadesmaeilketabforoosh2025blender,\n title={From blender to farm: Transforming controlled environment agriculture with synthetic data and SwinUNet for precision crop monitoring},\n author={Aghamohammadesmaeilketabforoosh, Kimia and Parfitt, Joshua and Nikan, Soodeh and Pearce, Joshua M.},\n journal={PLOS One},\n volume={20},\n pages={e0322189},\n year={2025},\n publisher={Public Library of Science}\n}", "data_size_bytes": 7328247, "num_files": 4, - "num_images": 1, + "num_images": 0, "source": "doi", "data_link": "https://osf.io/5kzcb/", "examples_image_url": "", @@ -19223,7 +19104,7 @@ "classes": [], "license": "other", "documentation": "https://doi.org/10.3390/rs18050790", - "citation": "FRAPPART, FREDERIC, YGORRA, BERTRAND, BAUP, FREDERIC, MARTIN-COMTE, ALEXIS, GROSS, KEVIN, FIEUZAL, REMY, BATTISTA, CLEMENT, & RIAZANOFF, SERGE. (2026). *Timeseries of multispectral and radar observations and vegetation indices from Sentinel-1, Sentinel-2 and Landsat-8 at field scale* [Dataset]. Recherche Data Gouv. https://doi.org/10.57745/QLKA8K", + "citation": "FRAPPART, FREDERIC, YGORRA, BERTRAND, BAUP, FREDERIC, MARTIN-COMTE, ALEXIS, GROSS, KEVIN, FIEUZAL, REMY, BATTISTA, CLEMENT, & RIAZANOFF, SERGE. (2026). *Timeseries of multispectral and radar observations and vegetation indices from Sentinel-1, Sentinel-2 and Landsat-8 at field scale* [Dataset]. Recherche Data Gouv. https://doi.org/10.57745/QLKA8K", "bibtex": "@article{baup20266,\n title={6 Years of SAR (Sentinel-1) and Optical (Sentinel 2, Landsat-8) Acquisitions over Agricultural Surfaces in Southwestern France},\n author={Baup, Frédéric and Fieuzal, Rémy and Ygorra, Bertrand and Frappart, Frédéric and Riazanoff, Serge and Martin-Comte, Alexis and Gorrab, Azza},\n journal={Remote Sensing},\n volume={18},\n pages={790},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 164629748, "num_files": 3, @@ -19308,7 +19189,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18091325", - "citation": "Liu, Q., Xiao, S., Huang, C., Li, S., Wu, Z., & Tao, L. (2026). *Spatial scale-up modeling of forest canopy water storage capacity by using multi-source remote sensing data: A case study in southern Jiangxi province* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19253926", + "citation": "Liu, Q., Xiao, S., Huang, C., Li, S., Wu, Z., & Tao, L. (2026). *Spatial scale-up modeling of forest canopy water storage capacity by using multi-source remote sensing data: A case study in southern Jiangxi province* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19253926", "bibtex": "@article{liu2026spatial,\n title={Spatial Scale-Up Modeling of Forest Canopy Water Storage Capacity by Using Multi-Source Remote Sensing Data: A Case Study in Southern Jiangxi Province},\n author={Liu, Quan and Xiao, Shengsheng and Huang, Chao and Li, Shun and Wu, Zhiwei and Tao, Lizhi},\n journal={Remote Sensing},\n volume={18},\n pages={1325},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 877447, "num_files": 1, @@ -19346,7 +19227,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18091352", - "citation": "Martín Ortiz, P., Iranzo, C., Alves, D. B., Montorio, R., & Pérez-Cabello, F. (2025). *Post-fire NDVI Time Series for Pinus halepensis in Aragón: Multi-year Monitoring and Baseline Comparisons for Resilience Assessment* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17183984", + "citation": "Martín Ortiz, P., Iranzo, C., Alves, D. B., Montorio, R., & Pérez-Cabello, F. (2025). *Post-fire NDVI Time Series for Pinus halepensis in Aragón: Multi-year Monitoring and Baseline Comparisons for Resilience Assessment* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17183984", "bibtex": "@article{martnortiz2026effect,\n title={Effect of Baseline Definition on Post-Fire Resilience Metrics Derived from Landsat Time Series in Pinus halepensis},\n author={Martín-Ortiz, Pedro and Iranzo, Cristian and Alves, Daniel Borini and Montorio, Raquel and Pérez-Cabello, Fernando},\n journal={Remote Sensing},\n volume={18},\n pages={1352},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 2576005, "num_files": 2, @@ -19387,7 +19268,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18101522", - "citation": "Cui, Z., & Han, L. (2026). *NDVI-AUC original dataset* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18905858", + "citation": "Cui, Z., & Han, L. (2026). *NDVI-AUC original dataset* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18905858", "bibtex": "@article{cui2026thresholded,\n title={A Thresholded NDVI-AUC Metric from Multi-Source Optical Time Series for Mapping Surface Soil Salt Content in Vegetated Coastal Areas},\n author={Cui, Zi’ang and Liu, Yazhou and Song, Rufei and Wang, Jingzhe and Zhang, Zipeng and Ge, Xiangyu and Liu, Fangbing and Wang, Zhengdong and Ding, Jianli and Wang, Jinjie and Han, Lijing},\n journal={Remote Sensing},\n volume={18},\n pages={1522},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 25837523, "num_files": 3, @@ -19420,7 +19301,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18101604", - "citation": "Xie, Chen, D., Sun, X., Chen, X., Wang, H., Wang, K., Li, W., Yu, H., Ma, J., & Yang, X. (2026). *Date for Remote Sensing Monitoring of Leaf Litterfall Dynamics in Eastern China's Subtropical Forests Using Field-Based Litterfall Data* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18884593", + "citation": "Xie, Chen, D., Sun, X., Chen, X., Wang, H., Wang, K., Li, W., Yu, H., Ma, J., & Yang, X. (2026). *Date for Remote Sensing Monitoring of Leaf Litterfall Dynamics in Eastern China's Subtropical Forests Using Field-Based Litterfall Data* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18884593", "bibtex": "@article{xie2026remote,\n title={Remote Sensing Monitoring of Leaf Litterfall Dynamics in Eastern China’s Subtropical Forests Using Field-Based Litterfall Data},\n author={Xie, Meizhen and Chen, Daosheng and Sun, Xiqing and Cheng, Xiaoyan and Wang, Huimin and Wang, Kehan and Li, Weiqiang and Yu, Hongwei and Ma, Jiahao and Yang, Xiaodong},\n journal={Remote Sensing},\n volume={18},\n pages={1604},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 120862, "num_files": 1, @@ -19459,7 +19340,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18111778", - "citation": "Yang, Y., & Wang, H. (2026). *Identification of Mown Grassland in the Xilingol League by Leveraging Multi-Modal Remote Sensing Data and the MAD-Net Model* [Dataset]. Mendeley Data. https://doi.org/10.17632/5S6JZZXWCN.1", + "citation": "Yang, Y., & Wang, H. (2026). *Identification of Mown Grassland in the Xilingol League by Leveraging Multi-Modal Remote Sensing Data and the MAD-Net Model* [Dataset]. Mendeley Data. https://doi.org/10.17632/5S6JZZXWCN.1", "bibtex": "@article{yang2026identification,\n title={Identification of Mown Grassland in the Xilingol League by Leveraging Multi-Modal Remote Sensing Data and the MAD-Net Model},\n author={Yang, Yalei and Wang, Hong and Li, Xiaobing and Wang, Yixuan and Tang, Zengwei and Jia, Zixuan and Wang, Ziru},\n journal={Remote Sensing},\n volume={18},\n pages={1778},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 60272728, "num_files": 1, @@ -19494,7 +19375,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18111782", - "citation": "Hu, Z., Dai, W., Xu, Y., Mei, yuqing, & Chen, X. (2026). *Spatiotemporal distribution dataset of aquaculture ponds and salt pans along the Jiangsu coast, China, 1985–2025* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19696441", + "citation": "Hu, Z., Dai, W., Xu, Y., Mei, yuqing, & Chen, X. (2026). *Spatiotemporal distribution dataset of aquaculture ponds and salt pans along the Jiangsu coast, China, 1985–2025* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19696441", "bibtex": "@article{hu2026mapping,\n title={Mapping 40 Years of Coastal Production Spaces: Spatiotemporal Co-Evolution of Aquaculture Ponds and Salt Pans Along the Jiangsu Coast, China (1985–2025)},\n author={Hu, Zichuan and Dai, Wen and Chen, Xinye and Mei, Yuqing and Sun, Jiangbing and Xu, Yansen},\n journal={Remote Sensing},\n volume={18},\n pages={1782},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 7611898, "num_files": 1, @@ -19535,7 +19416,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18132099", - "citation": "Orzan, L., Tomao, A., Casolo, V., Cingano, P., Král, K., Kratoš, F., Krůček, M., Trotta, G., Živec, M., & Alberti, G. (2026). *LiDARStructure_UnderstoryDiversity* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19914025", + "citation": "Orzan, L., Tomao, A., Casolo, V., Cingano, P., Král, K., Kratoš, F., Krůček, M., Trotta, G., Živec, M., & Alberti, G. (2026). *LiDARStructure_UnderstoryDiversity* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19914025", "bibtex": "@article{orzan2026high,\n title={High-Resolution LiDAR Reveals Scale-Dependent Links Between Forest Structure and Understory Plant Diversity Across Successional Stages},\n author={Orzan, Lorenzo and Tomao, Antonio and Casolo, Valentino and Cingano, Paolo and Král, Kamil and Kratoš, Filip and Krůček, Martin and Trotta, Giacomo and Živec, Miha and Alberti, Giorgio},\n journal={Remote Sensing},\n volume={18},\n pages={2099},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 21511, "num_files": 1, @@ -19568,7 +19449,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18132162", - "citation": "Silva, B. L. de C. e, Ferreira, K. R., Queiroz, G. R. de, Mota, J. S. da, Monteiro, E. C. S., Teodoro, M., Silva, I. C. de O., Silva, M. B. da, Inácio, R. D., Aluvei, R. A., Gomes, A. F., Almeida, C., & Adami, M. (2026). *Scalable Clustering-Based Vegetation Mapping from Satellite Image Time Series — Paper Compendium* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.20820627", + "citation": "Silva, B. L. de C. e, Ferreira, K. R., Queiroz, G. R. de, Mota, J. S. da, Monteiro, E. C. S., Teodoro, M., Silva, I. C. de O., Silva, M. B. da, Inácio, R. D., Aluvei, R. A., Gomes, A. F., Almeida, C., & Adami, M. (2026). *Scalable Clustering-Based Vegetation Mapping from Satellite Image Time Series — Paper Compendium* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.20820627", "bibtex": "@article{silva2026scalable,\n title={A Scalable Clustering-Based Method for Vegetation Mapping in Large Areas Using Satellite Image Time Series},\n author={Silva, Baggio Luiz de Castro e and Ferreira, Karine Reis and Queiroz, Gilberto Ribeiro de and da Mota, Juliana Santos and Monteiro, Erison C. S. and Teodoro, Mayara and de Oliveira Silva, Isabel Cristina and Silva, Murilo Brasil da and Inácio, Rodrigo Delgado and Aluvei, Rafael Andrade and Gomes, Agata Fabielle and Almeida, Claudio and Adami, Marcos},\n journal={Remote Sensing},\n volume={18},\n pages={2162},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 14641224096, "num_files": 4, @@ -19651,7 +19532,7 @@ "bibtex": "@article{tukhtamishov2026comprehensive,\n title={A Comprehensive Machine Learning Approach for Crop Classification Using Multi-Sensor Satellite Datasets and Multiple Vegetation Indices},\n author={Tukhtamishov, Oybek and Fawzy, Mohamed and Abdelmohsen, Karem and Barsi, Arpad and Kodirov, Rustambek and Foldvary, Lorant and Mamatkulov, Zokhid},\n journal={Remote Sensing},\n volume={18},\n pages={2571},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 104109274, "num_files": 36, - "num_images": 20, + "num_images": 0, "source": "doi", "data_link": "https://osf.io/jy4f8/", "examples_image_url": "", @@ -19738,49 +19619,14 @@ "documentation": "https://doi.org/10.3390/rs18030430", "citation": "", "bibtex": "@article{trung2026first,\n title={First Agriculture Land Use Map in Vietnam Using an Adaptive Weighted Combined Loss Function for UNET++},\n author={Trung, Ta Hoang and Ky, Nguyen Vu and Phan, Duong Cao and Minh, Duong Binh and Nguyen, Ho and Nasahara, Kenlo Nishida},\n journal={Remote Sensing},\n volume={18},\n pages={430},\n year={2026},\n publisher={MDPI}\n}", - "data_size_bytes": 172637, - "num_files": 10, + "data_size_bytes": 614709978, + "num_files": 3, "num_images": 2, "source": "doi", "data_link": "https://github.com/tatrung/AgrUNet", "examples_image_url": "", "hosted_in_original_form": true }, - { - "name": "Mekong_River_Delta_land_cover_classification", - "machine_learning_task": "unlabeled", - "agricultural_task": "land_use_classification", - "location": [ - "Mekong River Delta, Vietnam" - ], - "lat_lon": [], - "country": "Vietnam", - "environment": "field", - "real_or_synthetic": "real", - "crop_types": [ - "paddy rice" - ], - "sensor_modality": "multispectral", - "imaging_equipment": [ - "Landsat 8/9 OLI/TIRS C2 L1" - ], - "collection_period": "late July 2022, late December 2022, early March 2023, and late May 2023", - "platform": "satellite", - "input_data_format": "external", - "annotation_format": "none", - "classes": [], - "license": "mit", - "documentation": "https://doi.org/10.3390/rs18070989", - "citation": "", - "bibtex": "@article{bui2026advanced,\n title={Advanced Semi-Supervised Learning for Remote Sensing-Based Land Cover Classification in the Mekong River Delta, Vietnam},\n author={Bui, Hai-An and Hsu, Chih-Hua and Young, Hsu-Wen Vincent and Chen, Yi-Ying and Liou, Yuei-An},\n journal={Remote Sensing},\n volume={18},\n pages={989},\n year={2026},\n publisher={MDPI}\n}", - "data_size_bytes": 117397, - "num_files": 15, - "num_images": 1, - "source": "doi", - "data_link": "https://github.com/buihaian1403/Land-use-land-cover-classification", - "examples_image_url": "", - "hosted_in_original_form": true - }, { "name": "hyperspectral_band_selection_classification", "machine_learning_task": "ground_truth", @@ -19819,135 +19665,6 @@ "examples_image_url": "", "hosted_in_original_form": true }, - { - "name": "dea_woody_vegetation_classification", - "machine_learning_task": "unlabeled", - "agricultural_task": "", - "location": [ - "mid-Murrumbidgee River floodplain, southern New South Wales, Australia" - ], - "lat_lon": [ - "-34.333, 148.833" - ], - "country": "Australia", - "environment": "field", - "real_or_synthetic": "real", - "crop_types": [ - "river red gum" - ], - "sensor_modality": "multispectral", - "imaging_equipment": [ - "Landsat 5, 7, and 8" - ], - "collection_period": "1988 to 2023", - "platform": "satellite", - "input_data_format": "external", - "annotation_format": "none", - "classes": [], - "license": "mit", - "documentation": "https://doi.org/10.3390/rs18132069", - "citation": "", - "bibtex": "@article{toqeer2026long,\n title={Long-Term Automated Mapping of Woody-Vegetation Dynamics in Hydrologically Altered Floodplains: An Open Data Cube Workflow Using Digital Earth Australia},\n author={Toqeer, Abdullah and Hall, Andrew and Horta, Ana and Habiba, Ume and Wassens, Skye},\n journal={Remote Sensing},\n volume={18},\n pages={2069},\n year={2026},\n publisher={MDPI}\n}", - "data_size_bytes": 144199, - "num_files": 13, - "num_images": 0, - "source": "doi", - "data_link": "https://github.com/abbi776/dea-wetland-woody-change-detection", - "examples_image_url": "", - "hosted_in_original_form": true - }, - { - "name": "Ontario_forest_canopy_height_classification", - "machine_learning_task": "unlabeled", - "agricultural_task": "", - "location": [ - "Ontario, Canada" - ], - "lat_lon": [], - "country": "Canada", - "environment": "field", - "real_or_synthetic": "real", - "crop_types": [ - "Tamarack", - "Black Spruce", - "White Birch", - "Dwarf Birch", - "Willow", - "Black and White Spruce", - "Jack Pine", - "Balsam Fir", - "Tamarack", - "eastern White Cedar", - "Poplar", - "White Birch", - "Maple", - "Oak", - "Yellow Birch", - "White and Red Pine", - "mixed forests containing White Pine, Red Pine, Hemlock, White Cedar, Yellow Birch, Sugar and Red Maples, Basswood, and Red Oak" - ], - "sensor_modality": "multispectral", - "imaging_equipment": [ - "Landsat 7", - "Landsat 8", - "Sentinel-1", - "ALOS-PALSAR-2", - "GEDI" - ], - "collection_period": "2019–2020", - "platform": "satellite", - "input_data_format": "external", - "annotation_format": "none", - "classes": [], - "license": "apache-2.0", - "documentation": "https://doi.org/10.3390/rs18152477", - "citation": "", - "bibtex": "@article{bermudez2026integrated,\n title={An Integrated Deep Learning Approach to Estimate Canopy Height and Uncertainty by Combining Seasonal Optical, SAR and Limited GEDI LiDAR Data over Ontario’s Managed Forests, Canada},\n author={Bermudez, Jose and Rogers, Cheryl and Sothe, Camile and Cyr, Dominic and Gonsamo, Alemu},\n journal={Remote Sensing},\n volume={18},\n pages={2477},\n year={2026},\n publisher={MDPI}\n}", - "data_size_bytes": 16231, - "num_files": 3, - "num_images": 0, - "source": "doi", - "data_link": "https://github.com/MacRemoteSensing/gedi-guided-ch-uncertainty", - "examples_image_url": "", - "hosted_in_original_form": true - }, - { - "name": "UAV_multispectral_cabbage_classification", - "machine_learning_task": "ground_truth", - "agricultural_task": "crop_classification", - "location": [ - "Segyo-ri, Baebang-eup, Asan-si, Chungcheongnam-do, Republic of Korea" - ], - "lat_lon": [ - "36.333, 127.333" - ], - "country": "Republic of Korea", - "environment": "field", - "real_or_synthetic": "real", - "crop_types": [ - "cabbage" - ], - "sensor_modality": "multispectral", - "imaging_equipment": [ - "MicaSense RedEdge-MX camera (MicaSense Inc., Seattle, WA, USA)" - ], - "collection_period": "2022", - "platform": "uav", - "input_data_format": "external", - "annotation_format": "numericMeasurements", - "classes": [], - "license": "other", - "documentation": "https://doi.org/10.3390/rs18162811", - "citation": "", - "bibtex": "@article{jung2026spectral,\n title={Spectral-Consistency-Aware Evaluation of Deep Super-Resolution Methods for UAV Five-Band Multispectral Crop Imagery},\n author={Jung, Whanjo and Wi, Seung Hwan and Ryu, Jae-Hyun and Lee, Hoonsoo},\n journal={Remote Sensing},\n volume={18},\n pages={2811},\n year={2026},\n publisher={MDPI}\n}", - "data_size_bytes": 8045056, - "num_files": 16, - "num_images": 0, - "source": "doi", - "data_link": "https://aihub.or.kr/", - "examples_image_url": "", - "hosted_in_original_form": true - }, { "name": "Sierra_greening_gradient_classification", "machine_learning_task": "unlabeled", @@ -20011,8 +19728,8 @@ "documentation": "https://doi.org/10.3390/rs18030466", "citation": "", "bibtex": "@article{mutakabbir2026noah,\n title={NOAH: A Multi-Modal and Sensor Fusion Dataset for Generative Modeling in Remote Sensing},\n author={Mutakabbir, Abdul and Lung, Chung-Horng and Zaman, Marzia and Upadhyay, Darshana and Naik, Kshirasagar and Millard, Koreen and Ravichandran, Thambirajah and Purcell, Richard},\n journal={Remote Sensing},\n volume={18},\n pages={466},\n year={2026},\n publisher={MDPI}\n}", - "data_size_bytes": 18468889, - "num_files": 5945, + "data_size_bytes": 51560011, + "num_files": 22105, "num_images": 0, "source": "doi", "data_link": "https://huggingface.co/datasets/mutakabbirCarleton/NOAH-mini", @@ -20083,7 +19800,7 @@ "classes": [], "license": "cc-by-4.0", "documentation": "https://doi.org/10.3390/rs18132221", - "citation": "Healey, S., Yang, Z., & Bullock, E. (2026). *Code and Reference Data for DIAS (Disturbance Index Alert System) in Madagascar* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.21109969", + "citation": "Healey, S., Yang, Z., & Bullock, E. (2026). *Code and Reference Data for DIAS (Disturbance Index Alert System) in Madagascar* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.21109969", "bibtex": "@article{yang2026integrating,\n title={Integrating Commercial and Public Imagery to Accelerate Deforestation Alerts},\n author={Yang, Zhiqiang and Bullock, Eric L. and Lindquist, Erik J. and Andrianirina, Carole and Healey, Sean P.},\n journal={Remote Sensing},\n volume={18},\n pages={2221},\n year={2026},\n publisher={MDPI}\n}", "data_size_bytes": 379348, "num_files": 2, @@ -20092,5 +19809,303 @@ "data_link": "https://doi.org/10.5281/zenodo.21109969", "examples_image_url": "", "hosted_in_original_form": true + }, + { + "name": "cotton_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cotton" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 132, + "classes": [ + "Alternaria", + "Bacterial Blight", + "Cercospora", + "Grey Mildew", + "Healthy" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0324293", + "citation": "Bhagya Patil. (2021). *Cotton Leaf Dataset* [Dataset]. Mendeley. https://doi.org/10.17632/6HM6PC3Y43.2", + "bibtex": "@article{aslam2025multi,\n title={Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm},\n author={Aslam, Afira and Usman, Syed Muhammad and Zubair, Muhammad and Yasin, Amanullah and Owais, Muhammad and Hussain, Irfan},\n journal={PLOS One},\n volume={20},\n pages={e0324293},\n year={2025},\n publisher={Public Library of Science}\n}", + "zip_size_bytes": 1294185968, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/cotton_disease_classification", + "examples_image_url": "/img/agml/sample_images/cotton_disease_classification_sample.webp" + }, + { + "name": "BGM_YOLO_plant_disease_classification", + "machine_learning_task": "object_detection", + "agricultural_task": "disease_detection", + "location": [], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "bean", + "strawberry", + "tomato" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 5482, + "classes": [ + "豆类角斑病", + "豆类锈病", + "草莓角斑病", + "草莓炭疽果腐病", + "草莓花枯病", + "草莓灰霉病", + "草莓叶斑病", + "草莓白粉病（果实）", + "草莓白粉病（叶子）", + "番茄早疫病", + "番茄叶霉病", + "番茄红蜘蛛病" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0322750", + "citation": "Xie, J. (2025). *DIsease Dataset* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.28612433.V1", + "bibtex": "@article{yu2025bgm,\n title={BGM-YOLO: An accurate and efficient detector for detecting plant disease},\n author={Yu, Chenghai and Xie, Junhao and Tony, Fernandes Jean Adrian},\n journal={PLOS One},\n volume={20},\n pages={e0322750},\n year={2025},\n publisher={Public Library of Science}\n}", + "zip_size_bytes": 160945469, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/BGM_YOLO_plant_disease_classification", + "examples_image_url": "/img/agml/sample_images/BGM_YOLO_plant_disease_classification_sample.webp" + }, + { + "name": "corn_leaf_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "northern region, Anhui Province, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "corn" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "NIKON D810, 1001x1500 pixels" + ], + "collection_period": "", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 600, + "classes": [ + "Bipolaris_Maydis", + "Common_Rust", + "Curvularia_Lunata(wakker)boed_Spot", + "Northern-Leaf-Blight", + "Own-Spot", + "Sheath-Blight" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0336945", + "citation": "Bai, L. (2025). *A lightweight model and corn leaf disease recognition* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.30264037.V6", + "bibtex": "@article{bai2025lightweight,\n title={A lightweight model and corn leaf disease recognition},\n author={Bai, Lujie and Zhu, Shaoqiu and Gao, Haitao},\n journal={PLOS One},\n volume={20},\n pages={e0336945},\n year={2025},\n publisher={Public Library of Science}\n}", + "zip_size_bytes": 69238069, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/corn_leaf_disease_classification", + "examples_image_url": "/img/agml/sample_images/corn_leaf_disease_classification_sample.webp" + }, + { + "name": "cocoa_pod_disease_gh_image_classification", + "machine_learning_task": "object_detection", + "agricultural_task": "disease_classification", + "location": [], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cocoa" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 2436, + "classes": [ + null, + "Cocoa", + "Pod Borer", + "Black Pod Rot" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0348147", + "citation": "Techie-Menson, H., Asante, M., Marfo Missah, Y., Abdul-Salaam, G., & Opoku Oppong, S. (2026). *Cocoa Disease Datasets* (Version 1) [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.31294003.V1", + "bibtex": "@article{techiemenson2026enhanced,\n title={Enhanced convolutional block attention module with Learnable Gated Fusion (LGF-CBAM) for cocoa pod disease identification},\n author={Techie-Menson, Henry and Asante, Michael and Missah, Yaw Marfo and Abdul-Salaam, Gaddafi and Oppong, Stephen Opoku},\n journal={PLOS One},\n volume={21},\n pages={e0348147},\n year={2026},\n publisher={Public Library of Science}\n}", + "zip_size_bytes": 721455689, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/cocoa_pod_disease_gh_image_classification", + "examples_image_url": "/img/agml/sample_images/cocoa_pod_disease_gh_image_classification_sample.webp" + }, + { + "name": "ct_pestnet_crop_pest_classification_2", + "machine_learning_task": "image_classification", + "agricultural_task": "", + "location": [], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "ground", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 4508, + "classes": [ + "0", + "1", + "10", + "11", + "12", + "13", + "14", + "15", + "16", + "17", + "18", + "19", + "2", + "20", + "21", + "22", + "23", + "24", + "25", + "26", + "27", + "28", + "29", + "3", + "30", + "31", + "32", + "33", + "34", + "35", + "36", + "37", + "38", + "39", + "4", + "5", + "6", + "7", + "8", + "9" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0356061", + "citation": "Cao, W. (2026). *D0 dataset* (Version 1) [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.31768975.V1", + "bibtex": "@article{li2026ct,\n title={CT-PestNet: A novel crop pest classification model based on cross-scale feature distillation enhancement and vision transformer with local token interaction},\n author={Li, Youming and Cao, Weilin and Zhang, Liqiang},\n journal={PLOS One},\n volume={21},\n pages={e0356061},\n year={2026},\n publisher={Public Library of Science}\n}", + "zip_size_bytes": 126567214, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/ct_pestnet_crop_pest_classification_2", + "examples_image_url": "/img/agml/sample_images/ct_pestnet_crop_pest_classification_2_sample.webp" + }, + { + "name": "soybean_leaf_wilt_classification_2", + "machine_learning_task": "image_classification", + "agricultural_task": "crop_phenotyping", + "location": [ + "Jackson Springs, North Carolina, United States" + ], + "lat_lon": [], + "country": "United States", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "soybean" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "August and September 2020", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 1892, + "classes": [ + "0", + "1", + "2", + "3", + "4", + "5" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture15060614", + "citation": "Sanjana Banerjee, Paula Ramos, Chris Reberg-Horton, Steven Mirsky, Anna Locke, & Edgar Lobaton. (2023). *Study of Leaf Wilt in Soybean Plants* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.8256382", + "bibtex": "@article{banerjee2025multiple,\n title={A Multiple Instance Learning Approach to Study Leaf Wilt in Soybean Plants},\n author={Banerjee, Sanjana and Ramos, Paula and Reberg-Horton, Chris and Mirsky, Steven and Locke, Anna and Lobaton, Edgar},\n journal={Agriculture},\n volume={15},\n pages={614},\n year={2025},\n publisher={MDPI}\n}", + "zip_size_bytes": 698351621, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/soybean_leaf_wilt_classification_2", + "examples_image_url": "/img/agml/sample_images/soybean_leaf_wilt_classification_2_sample.webp" + }, + { + "name": "simta_rice_mapping_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "crop_detection", + "location": [ + "Arkansas River Basin, United States", + "Sacramento Valley, California, United States" + ], + "lat_lon": [], + "country": "United States", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "rice" + ], + "sensor_modality": "sar", + "imaging_equipment": [ + "Sentinel-1 satellite" + ], + "collection_period": "2017-2019", + "platform": "satellite", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 3024, + "classes": [ + "2019Canifornia", + "EImage", + "ThImage", + "TImage" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18081237", + "citation": "Liu, L., Deng, X., & Quan, T. (2025). *Rice Mapping Based on SAR Imagery – Arkansas River Basin, Sacramento Region* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17997718", + "bibtex": "@article{ren2026simta,\n title={SimTA: A Dual-Polarization SAR Time-Series Rice Field Mapping Model Based on Deep Feature-Level Fusion and Spatiotemporal Attention},\n author={Ren, Dong and Liang, Jiaxuan and Liu, Li and Wei, Pengliang and Yang, Lingbo and Wang, Lu and Sun, Hang and Zhang, Kehan and Qiu, Bingwen and Liu, Weiwei and Huang, Jingfeng},\n journal={Remote Sensing},\n volume={18},\n pages={1237},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 5400509019, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/simta_rice_mapping_classification", + "examples_image_url": "/img/agml/sample_images/simta_rice_mapping_classification_sample.webp" } ] diff --git a/static/img/agml/sample_images/BGM_YOLO_plant_disease_classification_sample.webp b/static/img/agml/sample_images/BGM_YOLO_plant_disease_classification_sample.webp new file mode 100644 index 0000000..3bceba7 Binary files /dev/null and b/static/img/agml/sample_images/BGM_YOLO_plant_disease_classification_sample.webp differ diff --git a/static/img/agml/sample_images/cocoa_pod_disease_gh_image_classification_sample.webp b/static/img/agml/sample_images/cocoa_pod_disease_gh_image_classification_sample.webp new file mode 100644 index 0000000..c8dbc59 Binary files /dev/null and b/static/img/agml/sample_images/cocoa_pod_disease_gh_image_classification_sample.webp differ diff --git a/static/img/agml/sample_images/corn_leaf_disease_classification_sample.webp b/static/img/agml/sample_images/corn_leaf_disease_classification_sample.webp new file mode 100644 index 0000000..73ff50b Binary files /dev/null and b/static/img/agml/sample_images/corn_leaf_disease_classification_sample.webp differ diff --git a/static/img/agml/sample_images/cotton_disease_classification_sample.webp b/static/img/agml/sample_images/cotton_disease_classification_sample.webp new file mode 100644 index 0000000..a4e0753 Binary files /dev/null and b/static/img/agml/sample_images/cotton_disease_classification_sample.webp differ diff --git a/static/img/agml/sample_images/ct_pestnet_crop_pest_classification_2_sample.webp b/static/img/agml/sample_images/ct_pestnet_crop_pest_classification_2_sample.webp new file mode 100644 index 0000000..2a24fdd Binary files /dev/null and b/static/img/agml/sample_images/ct_pestnet_crop_pest_classification_2_sample.webp differ diff --git a/static/img/agml/sample_images/simta_rice_mapping_classification_sample.webp b/static/img/agml/sample_images/simta_rice_mapping_classification_sample.webp new file mode 100644 index 0000000..a184168 Binary files /dev/null and b/static/img/agml/sample_images/simta_rice_mapping_classification_sample.webp differ diff --git a/static/img/agml/sample_images/soybean_leaf_wilt_classification_2_sample.webp b/static/img/agml/sample_images/soybean_leaf_wilt_classification_2_sample.webp new file mode 100644 index 0000000..c65a2c1 Binary files /dev/null and b/static/img/agml/sample_images/soybean_leaf_wilt_classification_2_sample.webp differ