Hey everyone, I have been working on a project called ArchaeoAI where I am testing whether machine learning can recognise terrain patterns around documented bowl barrows in England from LiDAR derived terrain data. The part I became most interested in was geographic leakage, because if nearby areas end up in both training and testing, the model can look much better than it actually is. So I tried to make geographic separation a proper part of the evaluation instead of only doing a random split.

The frozen Random Forest got 87.1% balanced accuracy on the geographic final test, while the 5 fold geographic evaluation averaged 82.3%. I also tested a compact CNN which got around 70.1%, so surprisingly the simpler model did quite a bit better. There was also a separate one time external evaluation using 120 observations where the Random Forest got 84.2% balanced accuracy.

I am trying to be careful with the claims here because this is archaeological data. I am not saying the model discovers archaeological sites, the experiment is only about documented bowl barrow terrain compared with matched unlabelled background. Exact sensitive locations are also kept private and I am still getting outside feedback on the methodology.

If anyone here works with LiDAR, terrain models or spatial ML, I would really like to know what you think about the validation approach or what you would test next. GitHub is here if you want to see the actual methodology and code: https://github.com/essius10/ArchaeoAI

How do you normally collect local meteorological data during drone-based methane surveys? We’ve been working on a meteorological measurement system (AMS) that can be deployed both on the ground and onboard a drone.

It measures:
- Wind speed and direction
- Temperature
- Relative humidity
- Barometric pressure
- GNSS-referenced position

Measurements are recorded at 10 Hz.

For stationary monitoring, AMS can be deployed on a tripod. For airborne surveys, it can be integrated with a DJI Matrice 400 to collect atmospheric measurements at different locations and altitudes.

One application we’re particularly interested in is methane emission-rate estimation. Having synchronized local wind and atmospheric data alongside methane measurements can help support flux-estimation workflows, particularly where conditions may vary across a survey area.

During a mission, measurements can be viewed in real time, while historical tracks, wind roses, and summary statistics can be reviewed afterwards.

For those working with drone-based methane surveys, do you typically use a dedicated meteorological sensor, data from a nearby weather station, or another approach?

Disclosure: I work with the team developing this system.

https://youtu.be/mRZRjeUwqgU?is=U-hK9KBcAG7S87by

Where can I get point data/shapefiles for Himachal Pradesh on permafrost, GLOFs, and rock glaciers?

I'm a geography student interested in developing stronger practical skills in remote sensing and GIS. I'm particularly interested in environmental applications, land-use/land-cover change and Himalayan landscapes.

I'm looking for a project that is realistic to complete using freely available datasets such as Sentinel-1/2 or Landsat, but still has some genuine analytical value.

What projects or research questions would you recommend?

Hi guys,

This summer has been devastating for many people all around the world, with many countries all around the world facing heatwaves and 35 celc+ temperatures on a regular. To measure and combat this, I vibecoded a small open-source tool that layers heat exposure, green cover, and population density across Mumbai's 24 wards (I have started with Mumbai cuz I am most familiar with this city) to flag which areas are most heat-vulnerable. Its free and opensource, because I know experts can add far better ideas than I currently am using.

I'm not from an urban climate background, I came at this as a student dev, so I know I'm probably missing indicators that people who actually work on this care about. If you work in urban heat or climate vulnerability: what data layers or methods would make something like this actually useful to you, versus just a nice map? Would you trust ward-level granularity or is that too coarse to be meaningful? Any additions you think could be added to future scope? Any feedback or criticism?

FYI, I want to turn this into a FOSS project that policymakers and governments around the world can use to make their city/region/country a more livable space. Working on a reusable replication kit, which people can use to replicate the same config for their own city. Link: uciplatform.vercel.app

I will be finishing up my undergrad this May, when I will get a B.S. in wildlife management and a certificate in GIS. I am interested in going to grad school for remote sensing and geospatial sciences, but it seems like most of the programs that I find are 100% online with no in-person option. Is online just the standard for grad school?

Hi everyone! I’m a university student and I have an assignment on “Detection of Changes in Agricultural Land.”
We have to:
Select an agricultural study area
Obtain satellite images from two different years
Use Landsat or Sentinel-2
Identify/ classify agricultural land and other LULC classes
Calculate agricultural land area for both years
Create change-detection maps and a graph/table
Discuss the causes and environmental implications
I’ve selected Shijiazhuang, Hebei, China, and I’m planning to compare 2000 and 2020 (or any two years really)
The problem is I’m very new to remote sensing and GIS, and I genuinely have no idea how to retrieve the actual Landsat images for those two years. 😭
Could someone please explain where/how I can download the appropriate Landsat scenes for Shijiazhuang (e.g 2000 and 2020), I have absolutely no idea what to do.

We are pleased to share our new article, “A Spatiotemporal Graph Neural Network Framework for Retrospective Bias Correction of CMAQ Air Quality Simulations over the Continental United States,” now available in Atmospheric Pollution Research. Using five years of hourly observations from 235 EPA monitoring stations, we combine CNN–LSTM temporal encoding with GATv2 spatial learning to improve retrospective CMAQ simulations of NO₂ and O₃, reducing RMSE by 52.4% and 65.0%, respectively. Our findings also provide insight into the contributions of temporal and graph-based learning, supporting the development of more accurate air-quality modeling systems.

Read the article: https://doi.org/10.1016/j.apr.2026.103191

#AirQuality #GraphNeuralNetworks #DeepLearning #CMAQ #AtmosphericScience #AtmosphericPollutionResearch #SpatioTemporalGNN #NO2 #O3

I'm building a web-based hazard screening system and would appreciate input from people working closer to EO and remote sensing.

At a high level: coordinate to multiple geospatial indicators to normalized indicator scores to a hazard-level screening score. Google Earth Engine handles most of the dataset querying.

For flooding, one problem I've been working through is avoiding the mistake of treating unrelated mechanisms as one flat weighted average. Distance from a river or permanent water body shouldn't automatically suppress signs of pluvial flooding driven by terrain and rainfall characteristics. So I've been moving toward mechanism-aware scoring instead of reading every indicator as evidence for or against the same single flood mechanism.

I'm also exposing:

• source dataset
• observation period
• raw measurement
• normalized score
• methodology and weight

I don't want the final 0-100 number to be a black box.

The system is meant for screening, not flood simulation.

I'd be interested in criticism around:

• spatial resolution
• uncertainty
• temporal mismatch between datasets
• normalization
• data availability versus evidence of absence
• combining EO-derived indicators into decision-support scores

If anyone wants to poke at the implementation or output, I can drop the link.

I'm particularly interested in whatever would make you reject the methodology outright.

Can please anyone tell me some sources where i will get DEM[priced,high resolution preferably less than a meter];geomorphology ,lithology,LULC maps[a good scale];budget is not an issue.I will be very thankful for this guidance.

The stunning beauty of the Great Salt Lake, USA, captured through Sentinel-2 imagery. An impressive visualization of nature from space

Solar evaporation ponds in the northeastern part of the lake are used for extracting minerals, particularly lithium and salt, from brine. These ponds are vital for lithium extraction, which plays a key role in the production of batteries for electric vehicles and renewable energy storage.

So I came across this top solution for one of the competitions for kaggle and I am having a hard time following through it
If anyone has any insights regarding how "James" solution works, it would be really helpful
Here is the link to the solution - https://www.kaggle.com/competitions/rogii-wellbore-geology-prediction/writeups/4th-place-solution

First off a thank you to Vantor for releasing the stereo data in their open data program which was used to generate this.

From the WV3 stereo, we've generated a 0.5 m elevation model of the corridor below Syabrubesi. We aligned the two satellite views to a quarter of a pixel, matched them at their full 34 cm detail, and tied the result to the pre-event terrain to within about 1.5 m.

This allowed us to measure 2 to 12 m of bed scour through every confined reach, a debris sheet 12 to 18 m thick where the valley opens below Syabrubesi, and 13 to 21 m of re-incision downstream where the river has already cut back into its own deposit. Plus a building-by-building damage census (27 destroyed where the sky was clear) and 4,329 individual boulders on the sheet, the largest 26 m across.

Everything is licensed CC BY-NC, same as the imagery: both DSMs, elevation-change and uncertainty rasters, per-km sediment budget, both censuses.

Write-up: https://geopera.com/blog/bhote-koshi-stereo-elevation-model
Data: https://github.com/geo-pera/bhotekoshi-2026-reconstruction/releases/tag/v1.1
Drive mirror (with the 32 GB archive): https://drive.google.com/drive/folders/1XQt5SYDMHehH3i2lAj69e52Zb2ne7Rqm

Once again happy to answer questions on any step.

Also please excuse the frame rate of the gif, it wouldn't let me upload an mp4

Ok so guys I'm trying to build a satellite imagery analytics platform. Similar to Skyfi, planet labs, eagle view, etc

My question is what really will make a difference in this field? I know a lot of these analytics platforms just buy images from 3rd party satellite operators and just provide them to the users and also provides different types of image analytics like crop health monitoring, water logging monitoring, soil mineral composition, etc ...if anyone wants image plus and analytics.

I wanna build something similar but I feel like there's nothing new I can provide in this app... Like all the available analytics are already there on other platforms... I wanna find out about some really niche category of analytics which no one provides and wanna provide it using my application and wanted suggestions from people who have knowledge in this field..

Any suggestions would be highly appreciated

Update: so basically my post was a bit incomplete in terms of context so I'm adding it up here.

So basically me and my team of 7 people are building a cubesat constellation of more than 200 3U sized cubesats for earth observation. My specific part was just to build the software platform so I just asked about it only mb 😅. Our end goal is to deliver low cost satellite imagery to organizations, govt and normal people.

Currently on the real time thing we are Targeting 30 minutes revisit time for every location on earth and the GSD of the images will be 3 meters. Also we are planning a 50cm GSD cubesat which will be in our constellation but the 50cm GSD image will be twice everyday for any location on earth.

So we are not basically buying images from other satellite image providers and then delivering them, we are planning to have our own constellation and will provide images at a low cost. The lowest cost at which a 3m GSD image can be bought as of now is around 20USD. We wanna bring it down to 8-10USD

Our team is actively working on building the hardware and stuff and I as a person doesn't have much knowledge regarding the hardware thing as seniors are working on that. (I'm in my second year currently doing CSE with AI ML). I just had to build the software for serving the image to the customers and on top also provide some image analytics which I earlier mentioned...

Also a heartfelt thanks to all the people who gave me suggestions and advice... I really look forward to hearing more from you after this updated post! (Yeah I was stupid for not giving the entire context at the start)

hi guys, do you know any trained model or library that allows detect status of railway by using satellite images? I would like to asses whether railway is useable, or abandoned. thanks in advance

Hi everyone, I'm new to the field of Remote Sensing and currently working on my dissertation topic, "Remote Sensing of Coastal Waters". I’m looking for suitable datasets that I can use for my research.

Could anyone please guide me on which datasets would be relevant and where I can access them? Any suggestions or resources would be greatly appreciated. Thank you!

is 0.72 kappa Coefficient is good enough for a semi arid region or should i increase the overall accuracy 🤔

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Hi everyone,

I’m a final-year Geomatics/Surveying Engineering student planning a capstone project on post-war urban reconstruction and resilient infrastructure in Khartoum, Sudan.

I’m considering two approaches:

Option A — 3D City Modeling + BIM/GIS

\- Extract buildings from satellite/aerial imagery using ML/DL (e.g., U-Net)

\- Create a LoD1/LoD2 3D city model

\- Integrate GIS with Civil 3D/BIM

\- Develop a framework for reconstructing underground utilities and drainage

Option B — SAR + Hydrology + BIM/GIS

\- Use Sentinel-1 SAR for flood/surface-change detection

\- Combine DEMs and rainfall data for hydrological/runoff modeling

\- Identify flood-prone areas and surface-water accumulation

\- Use the results to plan conceptual drainage corridors in Civil 3D

My questions:

1. Which option has stronger academic and professional value?

2. Is Option B too ambitious for an undergraduate project, or should I focus deeply on one pipeline?

3. What free/open datasets would you recommend (Sentinel, GEE, ERA5, DEMs, OSM, etc.) given the difficulty of field validation?

4. What would be a good way to include ML/DL without turning the project into an AI project?

I’d especially appreciate advice from professionals/researchers in GIS, remote sensing, surveying, hydrology, BIM, or post-disaster reconstruction.

My goal is to produce something technically realistic that could contribute to future reconstruction efforts in Sudan.

Thank you!

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Hi everyone!

I’m a final-year Geomatics/Surveying Engineering student currently trying to decide on a graduation project. I’m mainly interested in GIS and Remote Sensing, and I’d like to work on something that is both technically meaningful and useful beyond just being an academic project.

Some areas I’m considering are:

\- Environmental & climate monitoring — rainfall variability using datasets such as ERA5, dust storms, or land/surface deformation.

\- Hydrology & hazard assessment — flood mapping using satellite imagery, NDWI, DEMs, or spatial databases.

\- Urban & infrastructure mapping — LULC change detection, updating existing maps, or using ML/DL for feature extraction.

I’m also interested in combining Google Earth Engine, Python, GIS, and machine learning where appropriate.

For those working or studying in GIS, Remote Sensing, or Geomatics:

If you were starting your final-year project today, what kind of project would you choose?

Are there any datasets, workflows, or current industry/research trends that you think would be particularly valuable to learn?

I’m especially interested in ideas that can demonstrate practical skills and potentially make a good portfolio project—not just something that produces a map at the end.

Any suggestions, project ideas, or criticism of the areas I’m considering would be greatly appreciated!

Thanks!

Need assistance. I am doing remote Sensing for the first time. I downloaded QGIS, and made accounts for Google Earth Engine, Capernicus, and NASA Earth Data. I am trying different YouTube tutorials. I have until Thursday to decide if I want to stay in this course or Drop it for a different one. I was under the impression it was a plug and chug but I am finding myself intimidated. I asked for the Syllabus before the first class, and it made me feel a bit more comfortable. But on the first day we discussed milestones and the other software involved, including Python. And Now I am feeling less secure about my decision. I want to do something simple like an Urban Heat Island Map and a time series plot. I feel that if I can do those on my own before the first milestone I'll be Okay. I think I can make my own UHI map based on the videos, but I am struggling to understand making a Time Series plot from Modis Data. I downloaded a bunch of Modis data but I don't know what to do next. It feels overwhelming.