Feedback on my work done so far. #34
Replies: 11 comments 11 replies
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Thanks for your time and effort! I have a couple of questions regarding your approach:
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Hi @rawann31, I have used Landsat 8 satellite data for generating pseudo-DEMs from SWIR bands. Specifically, I use the following bands: SWIR1 (Band 6): 1.57–1.65 µm SWIR2 (Band 7): 2.11–2.29 µm How I Derived Elevation Information from SWIR Data The formula for pseudo-DEM generation is: Pseudo-DEM = SWIR2 / ( SWIR1 + ϵ ) Normalization: Normalized Pseudo-DEM = Pseudo-DEM − min(Pseudo-DEM) / (max(Pseudo-DEM) − min(Pseudo-DEM)) Integration with Water Mask: Testing with Planet Imagery: SWIR Data Integration: I used the SWIR band from USGS imagery to generate pseudo-DEMs and refine water masks. Method: I applied the same pseudo-DEM generation method (SWIR2/SWIR1 ratio) to USGS imagery. Results: The high resolution of Planet imagery allowed for more detailed elevation information, which improved segmentation accuracy in complex terrains (e.g., cliffs, shadows). The integration of SWIR data improved water detection accuracy, especially in areas with mixed pixels and shadows. The refined water masks were used as input for coastline extraction, resulting in more accurate and reliable results. Application to Deering, Alaska Difficulty Accessing Planet Imagery |
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Hi @rawann31 , Hoping to hear from you soon ! 😃 |
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Hello @Shashank-248 I am really sorry for replying late and thank you so much for your effort; I really appreciate it. First, I will explain how to access Planet imagery. You can create an account using your educational email, which will grant you a sufficient quota for a long time. Visit this link to find details on how to get free access. Click "Apply," (under Free or Basic section) fill in your details, and they will send you an email to activate your account within a week. Once your account is activated, you have two options for downloading imagery:
Additionally, in my previous work, I created a Python script that explains step-by-step how to download images using the API. You can find it here: DeeringAutoDownloadCode.py. This script explains everything in detail, including which notebook contains my code. If you have questions related to this send me any time. This part is easy but will take you some time to be familiar with |
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I am confused about something. Do you integrate Planet data with elevation data (derived from Landsat) from Landsat, or do you integrate elevation data (derived from Landsat) with Landsat bands? Please correct me if I have misunderstood. |
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Hi @rawann31, To clarify, I integrate elevation data derived from Landsat SWIR bands (specifically, the ratio of SWIR2 to SWIR1) with Landsat bands (e.g., Green, NIR, SWIR) to refine water masks. I do not directly integrate Planet data with elevation data from Landsat. My primary focus has been on using Landsat for both elevation approximation and water mask refinement. (I am planning to improve this further). Actually , I have used USGS imagery majorly, I did not work with Planet labs imagery , sorry for my miscommunication, but thanks for clarifying it. I will now use Planet imagery. Thankyou for taking time and explaining it to me. 😀 Correct me if I have gone wrong with my approach. |
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I have been looking actively into the codebase to find any bugs or issues to tackle with and understand the code better , I have seen that in rastertools.py script converts radiance , classifies and contour extracts, also calculating NDWI . But it may struggle with large datasets ( satellite imagery) and also lacks resampling and reprojection . I would like to improve this by adding chunk based processing , resampling , reprojection and logging . This is to handle large datasets efficiently , and better debug and track. What do you think about this? |
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hey @rawann31 , Can I send my proposal to your email once? I would like you to once check it so that I could submit it |
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Hey @fwitmer , @pradeeban Wanted to ask you if could you review my proposal through email. The deadline for GSOC contributor proposal application is nearing deadline and want to submit as soon as possible, also seek feedback from mentors . Could you check it out? @pradeeban , Nice to talk to you again . I have joined discussion in another project(Creating shareable "albums" from locally stored DICOM images) but soon found this project to suit my interests and strengths. Could you review my proposal if I could mail it to you, showcasing my understanding of the project's bigger picture and my work done towards it. Thanks, hoping to collaborate with you soon. |
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Hey @rawann31 , Could you check the proposal I have sent Thank you |
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I have come across other indexes- AWEI and WNDWI . I did not understand it clearly , could you explain me about it and about any improvements it might bring to our project over proposed MNDWI index. Does it really bring a significant improvement over MNDWI ( Which I have planned to implement ) , over place such as Deering , Alaska (Our primary area of interest). Thank you |
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Progress Update: Automated Coastline Extraction for Erosion Modeling in Alaska
I have been actively contributing to the Automated Coastline Extraction for Erosion Modeling in Alaska project and wanted to share my progress so far. My focus has been on improving the accuracy of coastline extraction using high-resolution PlanetLabs and Earth Explorer imagery. Below is a summary of my work, challenges faced, and planned next steps.
I would appreciate feedback on whether my approach aligns with the project’s goals and how I can further refine my contributions.
1. Work Done So Far
NDWI & MNDWI Calculation
Image: NDWI vs. MNDWI Comparison

Water Mask Refinement
Image: Water Mask Refinement (Before & After)

Visualization & Validation
Image: Extracted Coastline Result

Handling Challenging Conditions
2. Research & Problem Understanding
Project Analysis & Literature Review
Studied the existing NDWI-based segmentation pipeline and explored ways to improve its accuracy. Key research areas included:
3. Future Plans
Data Expansion
Improved Cliff Area Segmentation
Machine Learning Approaches
Integration with Existing Pipeline
4. Feedback Needed
@rawann31 , @fwitmer ,Looking forward to your thoughts!
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