From 57a38ed245b26c9ba710ea426fa8f3c58d07a1e8 Mon Sep 17 00:00:00 2001 From: Jared Smith Date: Wed, 30 Sep 2026 07:58:07 -0700 Subject: [PATCH 1/2] data --- static/data/hf_datasets.json | 42 ++++++++++++++++++ .../wheat_disease_detection_sample.webp | Bin 0 -> 325300 bytes 2 files changed, 42 insertions(+) create mode 100644 static/img/agml/sample_images/wheat_disease_detection_sample.webp diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index 19acfa4..8b8e3c4 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -17826,5 +17826,47 @@ "data_link": "https://doi.org/10.5281/zenodo.19949051", "examples_image_url": "", "hosted_in_original_form": true + }, + { + "name": "wheat_disease_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "disease_detection", + "location": [ + "Yuanyang, Henan Province, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "wheat" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Xiaomi 10S smartphone", + "Canon EOS 1500D camera" + ], + "collection_period": "February to late May 2023", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 4156, + "classes": [ + "0", + "1", + "2", + "3", + "4", + "5", + "6" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture15141526", + "citation": "Ma, C. (2024). *Wheat Dataset* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.15621359", + "bibtex": "@article{qian2025gdfc,\n title={GDFC-YOLO: An Efficient Perception Detection Model for Precise Wheat Disease Recognition},\n author={Qian, Jiawei and Dai, Chenxu and Ji, Zhanlin and Liu, Jinyun},\n journal={Agriculture},\n volume={15},\n pages={1526},\n year={2025},\n publisher={MDPI}\n}", + "zip_size_bytes": 1109856206, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/wheat_disease_detection", + "examples_image_url": "/img/agml/sample_images/wheat_disease_detection_sample.webp" } ] diff --git a/static/img/agml/sample_images/wheat_disease_detection_sample.webp b/static/img/agml/sample_images/wheat_disease_detection_sample.webp new file mode 100644 index 0000000000000000000000000000000000000000..7ca758974201096500c7679821aa2938cedbe463 GIT binary patch literal 325300 zcmZU)Q*Sonv_`e8#`H#GJewzUN 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b4fb3eb..6afecb3 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": "2026-09-29", + "generatedAt": "2026-09-30", "annotations": [ { "atPeriod": "May 30", @@ -213,6 +213,12 @@ "date": "2026-09-29", "datasetCount": 439, "imageCount": 7080615 + }, + { + "period": "Sep 30", + "date": "2026-09-30", + "datasetCount": 500, + "imageCount": 7097132 } ] } diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index 8b8e3c4..c923776 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -17868,5 +17868,2229 @@ "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/wheat_disease_detection", "examples_image_url": "/img/agml/sample_images/wheat_disease_detection_sample.webp" + }, + { + "name": "Yangtze_rice_planting_distribution", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "crop_classification", + "location": [ + "Middle and lower Yangtze River Basin, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "rice" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "2022-2024", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "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", + "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, + "num_images": 3, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.29336981.v2", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "deciduous_physiochemical_reflectance", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Madingley Wood, Cambridge, United Kingdom" + ], + "lat_lon": [ + "52.2173, 0.0486" + ], + "country": "United Kingdom", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Acer campestre", + "Acer pseudoplatanus", + "Betula pubescens", + "Corylus avellana", + "Crataegus monogyna", + "Fraxinus excelsior", + "Quercus robur", + "Ulmus glabra" + ], + "sensor_modality": "hyperspectral", + "imaging_equipment": [], + "collection_period": "", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18030444", + "citation": "Chen, L. 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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, + "num_images": 4, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.17803750", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "China_30m_land_cover", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "land_use_classification", + "location": [ + "Central Ningxia Hui Autonomous Region, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "mixed", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "1991-2020", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "classLabel", + "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", + "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, + "num_images": 208, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.18180184", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "tea_plantation_spatial_characterization", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "crop_segmentation", + "location": [ + "Xinyang City, Henan Province, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "mixed", + "crop_types": [ + "tea" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18091281", + "citation": "Wang, R. (2026). *A feature-optimized deep learning framework for mapping and spatial 1 characterization of tea plantations in complex mountain landscapes* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18360843", + "bibtex": "@article{wang2026feature,\n title={A Feature-Optimized Deep Learning Framework for Mapping and Spatial Characterization of Tea Plantations in Complex Mountain Landscapes},\n author={Wang, Ruyi and Zhang, Jixian and Lu, Xiaoping and Kang, Qi and Chi, Bowen and Li, Junfeng and Li, Yahang and Lou, Zhengfang},\n journal={Remote Sensing},\n volume={18},\n pages={1281},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 4139120563, + "num_files": 11, + "num_images": 3888, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.18360843", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "subalpine_grassland_dieback", + "machine_learning_task": "image_classification", + "agricultural_task": "", + "location": [ + "Hrubý Jeseník Mountains, Czech Republic" + ], + "lat_lon": [], + "country": "Czech Republic", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "1984–2024", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18091328", + "citation": "Kachalova, O. (2026). *Subalpine Grassland Dieback in Hruby Jesenik Mountains, Czech Republic* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19773962", + "bibtex": "@article{kachalova2026recurrent,\n title={Recurrent Climate-Driven Dieback of Subalpine Grasslands in Central Europe Detected from Multi-Decadal Landsat and Sentinel-2 Time Series},\n author={Kachalova, Olha and Řezník, Tomáš and Houška, Jakub and Řehoř, Jan and Trnka, Miroslav and Balek, Jan and Hédl, Radim},\n journal={Remote Sensing},\n volume={18},\n pages={1328},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 23705590, + "num_files": 3, + "num_images": 213, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.19773962", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "field_parcel_delineation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "crop_segmentation", + "location": [ + "Heilongjiang Province, China", + "Shandong Province, China", + "Zhejiang Province, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18111783", + "citation": "Duan, Y. (2025). *A field parcel dataset in China main cultivating area.* (Version v0.1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17953919", + "bibtex": "@article{duan2026topology,\n title={Topology-Aware Field Parcel Delineation: Bridging Deep Semantic Features and Geometric Constraints},\n author={Duan, Yaming and Zhao, Runze and Xu, Xiangde and Zhang, Jinshui},\n journal={Remote Sensing},\n volume={18},\n pages={1783},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 6411281105, + "num_files": 6, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.17953919", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "urban_tree_canopy_mapping", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [ + "Boyle Heights and City Terrace, Los Angeles, California, USA" + ], + "lat_lon": [], + "country": "USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "", + "platform": "aerial", + "input_data_format": "external", + "annotation_format": "classLabel", + "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", + "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, + "num_images": 1647, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.17459767", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "decametric_LAI_estimation", + "machine_learning_task": "ground_truth", + "agricultural_task": "crop_phenotyping", + "location": [ + "Barrax, Spain", + "Pshenichne, Ukraine", + "Collelongo, Italy", + "Maragua–Upper Tana, Kenya" + ], + "lat_lon": [], + "country": "Spain, Ukraine, Italy, Kenya", + "environment": "field", + "real_or_synthetic": "mixed", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "2019-2020", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [], + "license": "cc-0", + "documentation": "https://doi.org/10.3390/rs18132137", + "citation": "Meng, K. (2025). *Data used in MSDI* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.30246472.V1", + "bibtex": "@article{meng2026interpretable,\n title={An Interpretable Multi-Source Data Integration Framework for Prior-Guided Decametric-Resolution LAI Estimation},\n author={Meng, Ke and Zhang, Zhewei and Wang, Qi and Wu, Tongzhou and Song, Zhubeijia and Wei, Haodong and Wang, Cong and Yin, Gaofei and Xu, Baodong},\n journal={Remote Sensing},\n volume={18},\n pages={2137},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 1887910494, + "num_files": 99, + "num_images": 64, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.30246472.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "poland_forest_mapping", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [ + "Poland" + ], + "lat_lon": [], + "country": "Poland", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "ESA WorldCover, ESA WC" + ], + "collection_period": "2021-2022", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18132215", + "citation": "Shahbandeh, M., Kaim, D., & Kozak, J. 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