r/remotesensing

Hi everyone! I'm an ecology researcher and lecturer, and I primarily use satellite imagery and remote sensing in my research. While I have hands-on experience with satellite imagery, I'd like to deepen my understanding of how satellites work so I can better follow current developments in the field. Do you know of any books that could help me improve my understanding and keep up with upcoming advances? Preferably science books, but more accessible resources are welcome too! It can be in english or french.
Thanks everyone for your help!

Last month I gave a hands-on, day-long workshop on building GeoAI agentic apps, at the GeoAI 2026 conference in Ghent, Belgium. I'll be giving a longer version of the course in two weeks on maven. If interested check it out! https://geoagents-course.decision-labs.com/ Feel free to ask me any questions you may have about it! #agentic #mcp #a2a agui

https://preview.redd.it/2cw9sa5943bh1.jpg?width=1920&format=pjpg&auto=webp&s=5871441286cecd3d8e0393118347c8cd11b4872e

HI ive gone out on my own and passionate on Earth Observation + Education. I see a rather frustrating trend in the industry today - in trying to address it, I also do not want to waste time on Earth Observation training content that goes under utilized, spend my time in the real problems

Graduates aren't entering fast enough to keep pace with how quickly the tech is moving. Meanwhile, people are arriving from adjacent fields, never formally trained in GIS/Remote Sensing, but doing real geospatial work every single day. Organisations are asking everyone to do more with less.
So how do these people get trained? Two options, really. Generic online courses that don't go deep enough, or training that only teaches one specific tool, taught behind sales incentives for that tool.
Either way not giving the industry the technology agnostic, thought driven capability development it deserves.

When someone's only training is tool specific, they're locking themselves into a platform. They start pressing buttons because they can, not because they understand why. We get vendor lock in as it's too hard to re-train on something else, and we get users who can operate software but not the reasonings we were taught in university behind it.

I'm not saying industry training should replace university training. University trained practitioners stay vital. What I'm saying is there's a need RIGHT NOW that isn't being met: true capability development. The transferable kind. Technology agnostic, faster paced, and built to evolve, without the sales incentives that bend tool specific training out of shape.
We need a space where everyone's learning different tools for the same job - not just tool specific communities as we see now - so we can grow and change and adapt in the industry known for change.

So I'm trying to build it. The problem I am facing however is the platform and engagement - coursera and other training platforms seem quite siloed - i want something more engaging with students. SKOOL has been the closest so far - good balance between user engagement but also course structure. The engagement i see here on Reddit is absolutely amazing but its not really a platform where someone could engage and retain focus along a course? So my question - is it valuable if i push out free content free community but then only white label to departments or businesses my polished course content? I see the need out there in Earth Observation but lacking means to address an audience to get the knowledge out there, effectively any suggestions here would be appreciated. Just a solo consultant trying to get EO out to a broader audience

I'm currently working on a research project on LULC classification of Aizawl district (Mizoram, India) using Sentinel-2 Surface Reflectance (10 m), DEM, slope, aspect, spectral indices (NDVI, NDBI, MNDWI, NDRE), and Random Forest/XGBoost. My goal is to publish this work, so I'm trying to build a methodology that is scientifically robust rather than simply achieving high accuracy.

I'm facing several issues and would really appreciate guidance from anyone experienced in remote sensing or Earth Engine.

Current workflow
Sentinel-2 SR (10 m)
Dry season composites (Nov–Feb)
Median composite
Cloud masking
Features: Sentinel bands + DEM + Slope + Aspect + Spectral Indices
Classes: Dense Forest, Open Forest, Agriculture, Barren Land, Water Bodies, Urban
Issues I'm facing

1. Temporal generalization
Should I:

Train separate models for 2017, 2020, and 2025 and compare LULC changes?
Or combine training samples from all three years into a single model and evaluate whether it generalizes across time?

Has anyone successfully built a single Sentinel-2 model that performs well across multiple years?

2. Composite quality / tile seams

Some areas of my Sentinel-2 composites appear faded or radiometrically different, especially near what seem to be Sentinel tile boundaries. These areas often lead to incorrect classifications.

Is this expected when using median composites?
Would you recommend Quality Mosaic, Medoid, or another compositing strategy instead?
Is there a better cloud-masking workflow than the standard QA60/SCL approach?

3. Training sample collection

I'm using:

Sentinel false-color composites in QGIS to digitize Dense Forest and Open Forest.
Google Earth Pro historical imagery to identify Agriculture, Urban, Water, and Barren Land where they're easier to distinguish.

Is this considered an acceptable sampling methodology for a research paper, provided all samples correspond to the same time period?

I'm looking for advice from anyone who has worked on multi-temporal Sentinel-2 classification, Earth Engine, or remote sensing research.

If you've published work in this area or have practical experience, I'd really appreciate your suggestions or even a discussion. I'm still early enough in the project to improve the methodology before writing the paper.

I am working with the HOTC 2026 dataset — a hyperspectral video object tracking benchmark with 406 training videos and 75 validation videos, captured using three different snapshot cameras covering visible (16 bands), near-infrared (25 bands), and red-NIR (15 bands) ranges. Each frame is a 3D data cube: height × width × bands, at 25 FPS.

The standard use case is single-object tracking — you initialise with a bounding box in frame 1 and track the target through the video. Most published work adapts RGB trackers (Siamese networks, SAM2) to handle the extra spectral bands.

What I find interesting about this data is that each pixel carries a near-continuous spectral reflectance signature — a physical fingerprint of the material the object is made of, not just its colour. This is information that standard RGB tracking completely ignores.

What has already been done with this type of data:

- Band selection to reduce redundancy (picking the most informative bands per target)

- Siamese network and SAM2 adaptation from RGB trackers

- Spectral-spatial attention for better discrimination

- False-colour rendering for visualisation

What I am curious about:

Has anyone seen hyperspectral video data used for anything beyond standard object tracking? Things I have been wondering about:

Can spectral signatures be used for material classification on the fly during tracking (knowing not just where the target is but what it is made of)?

Has anyone tried using hyperspectral video for anomaly detection (finding objects that are spectrally inconsistent with their surroundings)?

Is there any work on using spectral change over time as a motion cue, rather than using spatial motion as the primary signal?

Any ideas for applications in camouflage detection, since spectrally similar objects that look identical in RGB can still differ in their near-infrared signature?

Happy to share more about the dataset if useful.

Hello all, as the title states, I'm curious if there is one or more discord servers you might recommend for remote sensing people?

Figured I'd branch out to reddit to promote a book I recently published on SAR analysis. My background is in imagery analysis for the US Government for approx 20 yrs and for the last 3+ years I have worked for commercial SAR provider, Umbra. If anybody is interested in the analysis side of SAR and not the heavy math and physics, this is a good read.

https://a.co/d/0hjdMdCK

I recently released PyMapStitcher 3, a desktop tool for downloading and stitching very large satellite map areas into georeferenced GeoTIFF / BigTIFF outputs.

In my own workflow, this was the only tool that could reliably deliver really large satellite maps without running into memory limits. That makes it a good companion for Mustatil, because PyMapStitcher can create huge map outputs first, and Mustatil can then be used for AI/GIS workflows like annotation, YOLO detection, training, large-image detection, and geospatial export.

https://py-map-stitcher.com

https://mustatil-ai.com

https://pymapstitcher.de

Main features:

• large-area satellite map downloading
• low-RAM stitching workflow
• GeoTIFF / BigTIFF export
• georeferencing / EPSG:3857 workflows
• CUDA / CuPy acceleration support
• desktop GUI
• QGIS plugin workflow
• Windows, Linux, macOS, Snap, PyPI and Conda installation options

GitHub:
https://github.com/tarekwasfy01/PyMapStitcher-3---Cuda-Maps-Downloader

QGIS Plugin:
https://plugins.qgis.org/plugins/PyMapStitcher2/

PyPI:
https://pypi.org/project/pymapstitcher/

Install with pip:

python -m pip install --upgrade pip
python -m pip install pymapstitcher
pymapstitcher

Conda package:
https://anaconda.org/channels/mustatil/packages/pymapstitcher/overview

Install with Conda:

conda install -c mustatil pymapstitcher
pymapstitcher

Windows installer:
https://github.com/tarekwasfy01/PyMapStitcher-3---Cuda-Maps-Downloader/releases/download/PyMapStitcher3/PyMapStitcher3_Offline_Setup.exe

Windows onedir full EXE:
https://gitlab.com/TWasfy/pymapstitcher-3/-/raw/main/PyMapStitcher3_OnedirEXE_Offline_Setup.exe?ref_type=heads

Linux DEB:
https://github.com/tarekwasfy01/PyMapStitcher-3---Cuda-Maps-Downloader/releases/download/PyMapStitcher3/pymapstitcher_3.0.0_amd64.deb

Snap Store:
https://snapcraft.io/pymapstitcher

macOS PKG:
https://github.com/tarekwasfy01/PyMapStitcher-3---Cuda-Maps-Downloader/releases/download/PyMapStitcher3/PyMapStitcher-3.0.0-macOS.pkg

Mustatil companion project:
https://github.com/tarekwasfy01/Mustatil-YOLO-AI-Model-Trainer-

Needing a contractor for some project work with data analysis etc. Please DM Me!

https://preview.redd.it/p1nu78oe4y8h1.png?width=597&format=png&auto=webp&s=9567c750adb3e2f388f6075937ebe5f246d60789

https://preview.redd.it/47dsr40h4y8h1.png?width=703&format=png&auto=webp&s=b023560e900eed7a4eeeb4cafaffd0ff62858020

In my first picture, the final prediction output after applying deep learning to satellite imagery shows a blocky pattern. How can I solve this? Can it be solved internally without using any filters or windows?
Note: I created patches during training
and used the second picture for the deep learning process.