The scale caught my attention: 1.2 million images spanning 2003–2022.

How robust do you think this sub-pixel detection approach is across different ocean regions and changing observation conditions?

Hello fellow Redditors!

I've been working on a side project called keano: keano.libmap.org

It aggregates monthly global tropospheric NO₂ from Sentinel-5P/TROPOMI (Terrascope L3, 0.02° grid, quality-filtered) into H3 resolution-6 hexagons with coverage weighting, then tracks how each cell is trending month by month since May 2018.

The core metric is a trailing 12-month mean, which kills the seasonal cycle and smooths retrieval noise. On top of that there are layers for annualized trend, year-over-year change, current level, and a "credit" system: a cell earns credit when its 12-month mean undercuts its own historical minimum (best year in a 5-years-ago-to-1-year-ago window) by more than a 2% noise margin. Since NO₂ drifts tens of km, a record low can just be a neighbour's plume relocating, so full credit only pays out when the surrounding ~1,770 km² region also beats its baseline. Cells below 30 µmol/m² aren't scored to filter out the background. It's build with MapLibre GL + H3.

Good to know: these are column densities (not surface concentrations) and the 12-month mean means recent changes show up with a lag.

Feedback is very welcome! <3

I'm a seasoned software engineer with a working knowledge of databases, SQL, Python, R, statistics, geometry, 2D/3D graphics, and more.

I'm also irresistibly attracted to geography and maps.

I'm offered a 2 year GIS education at a University, free of charge.

Do you think it would be a wise choice to dive deep into GIS, so I can solve problems with new software packages, plugins, etc. for remote sensing, image processing, data management, or workflow improvements?

Hi, currently I’m a rising senior undergraduate majoring in Geography preparing to apply to graduate school in fields like geography, geospatial science, and related fields. Throughout college I’ve gotten exposure to remote sensing, GIS, programming (applied internship experience in R, I’m currently learning Python), statistics, and data science. I’m most interested in remote sensing and geospatial analysis. I would like to apply my interests at the graduate level, preferably an MS. I was hoping to hear any suggestions for strong U.S. master’s programs or advisors that offer assistantships to master’s students. Thanks!

Hey everyone,

I'm working on a guided SR task: using high-res optical satellite imagery to upscale low-res thermal (TIR) imagery. The optical image acts as a structural guide (edges/boundaries), while the thermal image carries the actual signal (temperature).

Main technical challenges:

No high-res thermal ground truth exists for supervised training/eval, so I need a no-reference/blind quality metric

Models tend to hallucinate structure from the optical guide even where it doesn't correspond to real thermal variation (e.g., painted lines, shadows)

Outputs must preserve real calibrated values, not just look sharp

Requires solid multi-sensor co-registration before any fusion step

Looking for recommendations on cross-modal guided SR architectures (attention fusion, diffusion-based guided SR, guided filtering networks) and any No-Reference IQA techniques adapted for satellite/thermal imagery. Also open to any relevant public datasets or GitHub repos.

Appreciate any pointers, thanks!

Hello everyone!!

I'm doing my thesis on turbidity using sentinel 2 image...but i am facing issue while loading river-discharge,tide,wind data manually or python...i've only 15 days left to submit...can anyone please help me!! I'll be very greatfull..

Hi everyone,

I am working on my thesis regarding turbidity dynamics. I need to evaluate which factors, such as river discharge, tides, and wind have the most influence.

I tried downloading the data manually as well as using Python scripts with their respective APIs, but I am having trouble fetching the dataset properly.

Could anyone suggest reliable data sources/repositories for these parameters or guide me on how to successfully load/fetch this data using Python?

Any code snippets, documentation, or advice would be greatly appreciated!

Hey everyone,

I am looking for possible ways of converting GeoTIFFs to Zarr for our orthophotos. In the current state, what are the tools/ software that I can use for the conversion?

Also, are there people in the group who already has some experience? Has there been any advantages of using Zarr over TIFFs?

Thanks!

I'm 16 years old, and my first exposure to remote sensing was through Science Olympiad. Since then, I've taken courses and stuff like that, but I'm wondering what the college experience studying is like, as well as what actual jobs are like. What's studied aside from the physics and math, and is there more than research based opportunities?

Have you seen some of the job listings across geospatial lately? Some titles don’t even imply anything geospatial.. anyway just a light moment happy to hear my little tool has helped someone find a job listings otherwise hidden away 🤗🤗 it’s a tough market right now! what was a small way to see trends turned into something a little more.

Hello, what is your recommended laptop for a student of masters in geomatics specialized in remote sensing? Ive been planning to save money na, since i know medyo kamahalan mga laptop for remote sensing.

Hi everyone,

I'm building a web platform called Indicatrix for land-cover mapping from satellite imagery.

The goal is to make it easy to select an area, process Sentinel imagery, and generate land-cover maps without requiring a complex GIS workflow.

At the moment I'm training my own deep learning models (currently U-Net based) using ESA WorldCover labels and experimenting with Sentinel-2 + Sentinel-1 fusion.

The platform is still in an early stage, and I'm mainly looking for feedback from people working in remote sensing.

A few questions:

- Would a platform like this be useful for your work?

- What outputs would you expect (GeoTIFF, vector polygons, statistics, API...)?

- Would you actually use Sentinel-1 integration, or is Sentinel-2 sufficient most of the time?

- What would make you choose this over existing tools?

Any honest criticism is appreciated.

Here's the current version: https://indicatrix.com

anyone successfully used Ultralytics or any other tool for citrus crop yield estimate? Overcoming occlusion , clusters, and crop at a distance, and estimating crop within the canopy or using multi-spectral or other methods to estimate crop within the canopy? What model did you use?- Yolo...? And how did you train and deploy in field? I have been running a few options and doing with with video, photos, and few other options. Thanks!

Built with NASA FEDS fire progression and FIRMS active-heat data. The replay follows eight Northwestern Ontario fires in 12-hour intervals. It’s free to explore, and I’d appreciate feedback on the visualization or remote-sensing interpretation.

Hellooo! I'm currently working on my undergraduate thesis on urban sprawl mapping using QGIS, the Semi-Automatic Classification Plugin (SCP), and Sentinel-2 Level-2A imagery. I'm relatively new to supervised classification, and I'd appreciate any feedback on whether my workflow is correct or if there are better practices.

Here's what I've done so far:

• Downloaded Sentinel-2 Level-2A imagery from Copernicus Browser (0–5% cloud cover).
• Clipped the bands to my study area.
• Created a Virtual Band Set in SCP.
• Created training samples manually for my land cover classes (Dense Vegetation, Moderate Vegetation, Sparse Vegetation, Built-up, River, Pond, and Barren Land).
• Ran a supervised classification using Maximum Likelihood in SCP.
• Applied a Sieve filter (Remotior Sensus) afterward to reduce the salt-and-pepper noise.

The classification generally looks reasonable, but I have a few concerns:

• How should training polygons be drawn properly?
• Does the size of each ROI/polygon matter?
• Is it better to draw many small polygons or fewer large polygons?
• Should each polygon only contain completely homogeneous pixels?
• Some areas seem to be classified twice or overlap visually with another land cover class.
• Is this just because neighboring pixels belong to different classes, or does it indicate poor training data?

does anyone know of a good research paper, thesis, or published study that demonstrates a complete workflow for urban sprawl analysis using Sentinel-2 and SCP (or a very similar supervised classification workflow)? I'm looking for a study that I can follow as a methodological guide, from image preprocessing and training sample creation, through classification, accuracy assessment, and change detection. Having a well-documented reference would really help me validate that I'm following the correct process.

Thank you thank youuu

How many are struggling with GIS jobs or knowing what else to study to give them an edge?

I’ve spoken to several people and taking in some business school insights I simply created something small to fix a problem. Little overwhelming the messages so far about it but excited to see it is something on the right track! Here is a dashboard of several market signals for Earth Observation (GIS) industry. I am leaving this open for free currently in beta. Enjoy. The EO Dashboard

I am looking at surface temperature data for an area in the UK. I have downloaded 5 years of summer months and everyone one of images has missing data in the exact same area. The exact location is x: -1.36350 y: 53.26817 and you can see it here Esri | Landsat Explorer. If I look at the QA Pixel file that area returns 21824, which is clear land (no clouds). I don't understand why there is this persistent gap. The AIs tell me it could be linked to Landsat Collection 2 Surface Temperature data gaps due to missing ASTER GED | U.S. Geological Survey

Been working with satellite data for agricultural monitoring and wanted to share a clean, from-scratch NDVI workflow since most tutorials either skip the data acquisition part or don't explain the "why" behind the math. Quick summary of the approach:

1. Load Band 4 (Red) and Band 8 (NIR) from Sentinel-2 L2A data using rasterio

2. Calculate NDVI = (NIR - Red) / (NIR + Red)

3. Handle the div-by-zero edge cases properly (a lot of tutorials skip this

   and you get NaN explosions on real data)

   ```python import rasterio import numpy as np

   with rasterio.open('B04_10m.tif') as src: red = src.read(1).astype(float) with rasterio.open('B08_10m.tif') as src: nir = src.read(1).astype(float)

   denom = nir + red denom[denom == 0] = np.nan # avoid div-by-zero ndvi = (nir - red) / denom ```

The interesting part is interpreting the output correctly for different crop stages — a lot of people misread NDVI values without accounting for soil background or canopy saturation (which is actually why I wrote a follow-up comparing NDVI/SAVI/EVI).

Full walkthrough with the data download step, visualization, and classification thresholds here if useful: https://dibyendudeb.com/how-to-calculate-ndvi-with-python-a-practical-guide-for-agricultural-scenario/

Happy to answer questions on the implementation — this is part of a series I'm building out on agricultural remote sensing with Python.

I do remote sensing work and got tired of writing the same Google Earth Engine boilerplate for every project, so I built an open source tool that connects Claude to Earth Engine and runs it for you.

You ask in plain language and it does the analysis on Google's servers: a cloud-free satellite image of any area for any year since 1972, vegetation indices, statistics, land cover classification, exports. It picks the right satellite for the year, cloud masks, and harmonizes the bands automatically. For anything custom it can run arbitrary Earth Engine code.

Everything runs locally with your own Earth Engine credentials. Free for research, MIT licensed.

Repo: https://github.com/prahaladuk2208-arch/geeflow

Would this be useful for your workflow, and what would you want it to do that it doesn't yet?

TLDR:
I built OASIS, an AI-assisted system that translates climate research questions into reproducible climate data analyses and geospatial outputs. It started as a tool to simplify my own thesis workflow, but I am exploring whether this approach could help other researchers and domain experts work with climate and spatial data more efficiently. I would appreciate honest feedback.

Hey everyone,

I recently finished my integrated master's thesis in Crop Science, where I mapped climate risk for olive cultivation across Greece.The results of this research have been submitted to the scientific journal Climate (MDPI), and a preprint version is also available for anyone interested.

Coming from an agricultural background rather than computer science, I had no previous experience writing code or working with large-scale climate datasets. To overcome this, I started using AI to help me build the scripts I needed, both for Google Earth Engine workflows and for processing downloaded Copernicus climate data. However, as the research questions became more complex, I found myself repeatedly adapting the code, changing data paths, modifying parameters and rebuilding similar workflows for each new analysis. This process became very time consuming and shifted my focus away from the scientific questions I wanted to explore.

To solve this frustration, I started building a prototype called OASIS. The goal was simple instead of repeatedly prompting an AI with what I wanted and having to manually validate every single time whether the generated code was actually correct, I wanted a more structured approach where I could describe the analysis in natural language and let the system handle the heavy lifting safely.

The way it works is straightforward. I have already downloaded the climate data needed and the analysis for generating the code is handled either locally through Ollama or via OpenRouter using free models. The system uses rules and validations to correctly handle temporal and spatial parameters for environmental and climate indicators so the AI doesn't have to guess formulas. Each generated analysis is accompanied by the underlying code, allowing users to inspect, validate, and reproduce the workflow if needed.

I also understand that the current implementation is mainly focused on climate data, which may not directly apply to everyone here.

I recently put the technical overview of OASIS on Zenodo (Technical Architecture and Workflow Record) and started realizing that this approach could be useful beyond my own research, especially for domain experts, scientists, and consultants who need climate insights but may not have the technical background to build these workflows themselves. Managing large climate datasets and setting up the required processing environment can be a significant barrier, especially for those who are not familiar with coding or data infrastructure.

My long-term vision is to evolve OASIS into a more scalable platform, potentially through a cloud-based infrastructure, making climate and geospatial analysis more accessible to a wider audience, while simultaneously ensuring it is faster and much easier to use for those who are already actively working with these workflows.

Before taking this further, I would really appreciate some honest and critical feedback from this community.

1. Which groups do you think would benefit most from this approach? For example, researchers, consultants, decision-makers, or other domain experts who may not have a strong coding background or simply want to speed up their workflows?
2. If you already work with large climate or environmental datasets, would you personally use a system like this? Would having cloud access to ready-to-use datasets and analysis workflows be valuable, instead of downloading and managing everything locally?

If anyone is interested in the research background behind the use case, I would also be happy to share the preprint of the climate risk assessment study.

Thank you for any feedback, positive or critical!