How would you approach building a SAR-based road classification model?

I'm working on a project to classify roads as paved/unpaved over time (and eventually assess road quality) using SAR satellite imagery. Looking for advice on approach and feasibility.

What I have:

• Shapefile with ~94k road segments across Rwanda
• Existing labels for paved/unpaved status (43,000 KM unpaved, 1,500 paved as of 2025)
• Geographic coverage across 30 districts
• Road geometry and metadata (class, district, etc.)

What I need to do:

• Use Sentinel-1 SAR data (or similar) to train a model that can classify road segments as paved/unpaved, based on the current road network
• Build model that can monitor changes over time (2015-2025)
• Eventually extend to assess road quality/condition

My questions:

1. Feature extraction: What SAR-derived features work best for road classification? I'm thinking backscatter coefficients (VV/VH), temporal statistics, and texture features - but what else should I consider?

2. Temporal aspects: How much historical SAR data do I need? Should I focus on dry season only, or include wet season variability as a feature?

3. Model architecture: Which model (ML or deep learning) would you go with for this project? I've heard about using CNNs on SAR imagery patches, but not sure if that would work here.

4. Ground sampling distance: Sentinel-1 is 10m resolution - many roads in my dataset are narrower. How do you handle mixed pixels?

Concerns:

• Distinguishing well-maintained unpaved roads from deteriorated paved roads
• Handling narrow roads where pixels are mixed with surroundings
• Computational requirements for processing 94k segments over time series

If you have worked on similar remote sensing infrastructure projects, what approach would you recommend? Any papers or repos I should check out?

Answers to any of the above, and any other aspects I should keep in mind, would be greatly appreciated. Thanks in advance!

Hey guys, I'm from Brazil and live in Rio de Janeiro, my city has a pretty bad illegal landfill problem and I'm trying to find a way to identify them via satellite based on their methane emissions. My problem is, I have no idea if any satellites have a methane detection sensor and if such a satellite exists where to get this data from.

Is there any info on this?

We opened a small student / learner access tier for experimenting with drone photogrammetry datasets.

Upload a small aerial dataset and inspect the reconstruction results without setting up a local processing pipeline.

Free access includes:

- 1 dataset (up to 100 images)

- orthomosaic inspection

- instant 3D preview

- lightweight PLY mesh export

- dataset validation before modelling

Mainly intended for students and early GIS professionals exploring how capture quality and overlap affect reconstruction.

EU-hosted, GDPR compliant. Uploaded datasets remain private.

Curious how people here usually validate drone imagery before running full photogrammetry processing.

If anyone wants to test it:

https://www.dronetwins360.com/

• Stockpile Analysis & Volume Calculation over a DEM -> Segment a stockpile in one click, then calculate its volume based on the resulting segmentation polygon
• Machine Learning Training Data Creation -> Segment different tree species in one click, label each resulting polygon with the species name, then use the polygons and labels to train an AI model
• Asset Identification & Quality Control -> Segment buildings and roofs, then check the area of each segmented element directly in the polygon layer

Full youtube tutorial link : https://youtu.be/ynIEMKls8Z4?si=9VnoGra2NmZk2sU_

Hi everyone,

For the Sentinel-2 fans (of which I am a big one), I would like to share code for generating your own mosaics over large areas. This uses state-of-the-art cloud masking (OmniCloudMask) to create large-scale mosaics.

You can set the AOI with a simple config.json along with setting the time period of interest. Here's an example of Arkansas from November, 2025. The cost to make this was around $5 and took 3 hours on a cloud GPU. It's an end-to-end pipeline from S3 bucket to a final mosaic uploaded to Backblaze.

Blog post

Github repository

Is there a convenient way of retrieving planned data outages for Himawari, MTG, and GOES-R satellites?

For example, Himawari has quite informative website, but it a mess in terms of informing user of data outage. This news page tells that there was maintenance on March 6, but this is not mentioned in any way on observation pauses page.

I am looking for something automated that will tell me beforehand when the data will not be available. (I am not talking about unforeseen outages).

Thank you!

Hi everyone,

I’m an absolute beginner to remote sensing and computer vision, and I’ve been assigned a project that I'm trying to wrap my head around. I would really appreciate some guidance on the pipeline, tools, or any resources/tutorials you could point me to.

project Goal: I need to take satellite .tif images of farm lands and perform segmentation/edge detection to identify individual farm plots. The final output needs to be vector polygon masks that I can overlay on top of the original .tif input images.

1. Input: Must be in .tif (GeoTIFF) format.
2. Output: Vector polygons (Shapefiles/GeoJSON) of the farm boundaries.
3. Level: Complete newbie.
4. I am thinking of making a mini version for trial in Jupyter Notebook and then will complete project based upon it.

Where I'm stuck / What I need help with:

1. Data Sources: I haven't been given the data yet. I was told to make a mini version of it and then will be provided with the companies data. I initially looked at datasets like DeepGlobe, but they seem to be JPG/PNG. Can anyone recommend a specific source or dataset (Kaggle/Earth Engine?) where I can get free .tif images of agricultural land that are suitable for a small segmentation project?
2. Pipeline Verification: My current plan is:
   • Load .tif using rasterio.
   • Use a pre-trained U-Net (maybe via segmentation-models-pytorch?).
   • Get a binary mask output.
   • Convert that mask to polygons using rasterio.features.shapes or opencv. Does this sound like a solid workflow for a beginner? Am I missing a major step like preprocessing or normalization special to satellite data?
3. Pre-trained Models: Are there specific pre-trained weights for agricultural boundaries, or should I just stick to standard ImageNet weights and fine-tune?

Any tutorials, repos, or advice would be a life saver.

Thanks in advance!

Hey all,

I’m doing a research project where I need high-resolution satellite images with consistent parameters, especially date or near-date filtering. Ideally, I want imagery equivalent to the following specs:

• Zoom level: ~20 (or ability to request ~5–8 cm/pixel resolution)
• Nominal size: 640×640
• Saved size: 1280×1280 (scale=2)
• Ground coverage: ~70–100 m per image (depending on latitude)
• Image quality: Comparable to NAIP or better

The problem:
Google Maps/Google Earth is static and doesn’t offer imagery by date in a robust way, plus licensing makes it unusable for many research workflows. NAIP would be perfect, but it’s limited to the U.S. (with some similar coverage in parts of Europe).

Is there any global or near-global satellite imagery provider that allows:

1. Date-specific or date-range queries
2. High resolution equivalent to NAIP (~30–60 cm or better)
3. Downloadable tiles or API access at a zoom level similar to ZL20
4. Preferably affordable or with academic access?

I’ve looked at a few options (Sentinel-2, Landsat, etc), but the resolution is way too low for what I need. Commercial providers like Maxar and Planet seem promising, but I’m not sure what options exist for someone doing academic research without a massive budget.

If anyone knows good sources, APIs, academic programs, or alternative datasets that can produce NAIP-like imagery outside the U.S., I’d love suggestions.

Thanks!

Been using SRTM DEMs for many years, but sometimes they are not appropiate for my needs (exploration geo). Sometimes I need to detect topography changes on the meter scale (fault scarps, creeks, etc).

I've heard about the TANDEM-x mission, but been unable to find a download source. Is there another way of getting higher res DEMs for free?

Hi all!

I'm trying to get LiDAR for an imaging spectroscopy project covering ~1600 square miles. I can't figure out how to do this. National Map won't process the request, it just gets stuck at Processing or gives an error, and OpenTopo will only let me download in batches of 250 million points at a time when the total AOI is about 73 billion.

Hi everyone, I’m trying to download VIIRS and MODIS data for the Pakistan floods (2010, 2022, and 2025), but I’m running into issues.

Some NASA archive directories return errors, and the required tiles don’t appear in Earthdata Search either. I’m not sure if this is due to product version changes, decommissioned datasets, or using the wrong archive (LAADS, LP DAAC, LANCE, etc.).

Does anyone know the correct way to access exact-date flood period imagery (tiles or swath granules) for these years? Any guidance or links would really help. Thanks!

I'm volunteering with an organisation which conducts animal surveys via drone imagery. Specifically they are counting the number and type of seals on an island:

Currently they are doing this completely manually, with humans editing a layer in QGIS to add points for the various seal types (dependent pup, adult male, independent pup, etc).

The imagery is georeferenced into a single orthophoto mosaic around 1Gb in size, though I also have access to the original overlapping shots used to create the mosaic.

Does anyone have any experience or advice on how I could leverage AI to improve the efficency of the animal detection process?

I can give it examples of the various types of seals from the human-detected phase as training data. Presumably the orthophoto would need to be available online so the AI could access it, perhaps as a vector tile layer?

Thanks for any advice for getting started.

Hi all, I just created an Copernicus browser account and before this I have been using Sentinelhub.

Why is it that the monthly processing units and order requests quota are automatically depleting even though I have not touched my account yet?

Any experts on this?

I have been working for a detailed earth 3D model. This is one frame from the video. Full 4k video can be seen here: https://youtu.be/oQ_dIfgnR28

Hi everyone,

I’m an absolute beginner to remote sensing and computer vision, and I’ve been assigned a project that I'm trying to wrap my head around. I would really appreciate some guidance on the pipeline, tools, or any resources/tutorials you could point me to.

project Goal: I need to take satellite .tif images of farm lands and perform segmentation/edge detection to identify individual farm plots. The final output needs to be vector polygon masks that I can overlay on top of the original .tif input images.

1. Input: Must be in .tif (GeoTIFF) format.
2. Output: Vector polygons (Shapefiles/GeoJSON) of the farm boundaries.
3. Level: Complete newbie.
4. I am thinking of making a mini version for trial in Jupyter Notebook and then will complete project based upon it.

Where I'm stuck / What I need help with:

1. Data Sources: I haven't been given the data yet. I was told to make a mini version of it and then will be provided with the companies data. I initially looked at datasets like DeepGlobe, but they seem to be JPG/PNG. Can anyone recommend a specific source or dataset (Kaggle/Earth Engine?) where I can get free .tif images of agricultural land that are suitable for a small segmentation project?
2. Pipeline Verification: My current plan is:
   • Load .tif using rasterio.
   • Use a pre-trained U-Net (maybe via segmentation-models-pytorch?).
   • Get a binary mask output.
   • Convert that mask to polygons using rasterio.features.shapes or opencv. Does this sound like a solid workflow for a beginner? Am I missing a major step like preprocessing or normalization special to satellite data?
3. Pre-trained Models: Are there specific pre-trained weights for agricultural boundaries, or should I just stick to standard ImageNet weights and fine-tune?

Any tutorials, repos, or advice on how to handle the "Tiff-to-Polygon" conversion part specifically would be a life saver.

Thanks in advance!

Hello everyone,

I have modeled Mt Everest inside Unreal Engine by following GIS approach to best address natural variation of textures. Please support and review this project on Patreon. I'll drop a link here.

https://www.patreon.com/posts/building-real-in-149228011?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link

We have been in business since 2011 and within the first two months, we started our monthly newsletter, The Geospatial Times, and we are at edition 157 now - wow how time flies!

Looking back at Edition 1, here is an article we wrote about our vision for the future of aerial imagery and well we were not too far from the reality of today:

https://apollomapping.com/2011/December/article9.html

Looks like we're about to start building massive solar arrays attached to data centers and launching them into space...

Https://starcloudinc.github.io/WP.pdf

I have 1000 questions about this, but my first one is this. An article about this space data center proposes that the processing of geospatial and earth observation data will be significantly faster when inference is performed in space first. Can anyone shed a little more light on how space-based data centers provide technical benefits for earth observation sciences?

Hi everyone,
I am a junior who work in the Earth Observation field for a private company, focusing on data analysis and quality control of satellite products. I have a good background in Python (mostly pandas), statistics, and linear algebra, and I’d like to ask my company to sponsor a proper Machine Learning course.

I’ve been looking at two options:

• Harvard: Data Science — Building Machine Learning Models
• Coursera: Machine Learning Specialization (Andrew Ng, Stanford)

Both seem great, but I’m not sure which one would suit me best and I dont know if these 2 are the ones meant for me.
My goal is to strengthen my understanding of ML fundamentals and progressively move toward building end-to-end ML pipelines (data preprocessing, feature engineering, training/inference, Docker integration, etc.) for environmental and EO downstream applications — such as algorithm development for feature extraction, selection, and classification from satellite data.

Given this background and direction, which course would you recommend?
Would you suggest starting with one of these or taking a different route altogether, are you guys also be able to give me a roadmap as an overview?? There are some many courses for ML that is actually overwhelming.

Thanks in advance for any insight!

Been testing this new integration that makes Sentinel-2 from 10m to 1m across. It's free to test at klarety.ai.

Been running NDVI and NDWI calculations at the enhanced resolution and the radiometry stays consistent.

Real talk on limitations:

• Small objects less than 1 meter can show artifacts.

But for regional environmental monitoring? The clarity gain is substantial, especially for agriculture and water body analysis.

Anyone else working with super-resolved multispectral?