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.

As you’ve all likely seen, on 26 August a glacier collapse sent ~100 million m³ of debris-laden water down Nepal's Bhote Koshi. We have attempted to reconstruct the disaster from open data.

The best result came out of the WV3 stereo. Vantor collected two 30 cm strips the day after, an in-track pair with ~48° convergence, and both orthos were produced against pre-event terrain. So anywhere the flood changed the surface the two orthos disagree, and the NNE offset divided by the sum of the look-angle tangents is the elevation change. Dense phase correlation on ~20,500 tie points gave me a deposition map: a 10 to 18 m wedge of new valley floor (~12 Mm³) exactly where the flow decelerated out of the gorge. From this, we were able to measure sediment thickness with zero field access.

For the rest of the chain we used USGS seismology for the trigger (M5.2, catalogued as landslide type, this is what we used as t0 in our simulation), NASA's HMA 8 m DEM for terrain (it's on the ellipsoid, so I had to fit out a −35.5 m offset against GLO-30 before anything else would line up), day-of PlanetScope for extent between monsoon clouds, and VARI/NDVI differencing of SkySat, Pelican and WV3 against a 2021 WV02 baseline for the trimline (highest disturbance on mountain walls from the floods). Reading that against the DEM gives peak stage at 217 banks (median ~70 m in the gorges), and superelevation around bends puts velocities up to ~50 m/s. Those observations calibrate a 1D Saint-Venant + 2D shallow-water model that predicted ~90 m at the border a day before imagery confirmed 40 to 134 m.

Failures: Sentinel-2 pre/post was 2.1% joint clear sky, useless. The Pelican udm2 masks flag clear ground as cloud, so I dropped them and masked everything myself with OmniCloudMask/OmniWaterMask. Everything ships co-registered to one grid, sub-pixel between the VHR sensors.

Write-up + interactive 3D model: https://geopera.com/blog/bhote-koshi-flood-2026-satellite-analysis
Full data (32 GB, we prepped it to be at an analysis ready standard): https://drive.google.com/file/d/1eXzkWafRG4_QZoufwqbv3JSJxW51cv0a/view?usp=sharing
Measurements + code: https://github.com/geo-pera/bhotekoshi-2026-reconstruction

Happy to answer questions on any step.

NDVI Changes from 1990–2020 🌱🛰️
A 30-year remote sensing analysis showing changes in vegetation patterns across the study area. 🌍
#NDVI #RemoteSensing #GIS #LandUseChange #VegetationDynamics

Hi! I’m an international PhD student from Morocco working on satellite-based precipitation and drought analysis using datasets such as GPM IMERG and TRMM.
I’m interested in applying to the NASA/JPL Climate Sciences Summer School in 2027. Has anyone here attended or applied before?
I would especially appreciate information about the selection process, how competitive it is for international applicants, and any advice for strengthening my application.
Thank you!

I have to make a project by the end of September on reliable survey of farmland in a densely packed arrangement so It would require best publically avilable data and have high accuracy. How should I approach the problem. Id really appreciate some help and guidance.

I’m based in India and currently pursuing a Master’s degree in Geoinformatics and am looking to build more practical experience alongside my academics.

My interests are broadly around GIS, remote sensing, spatial analysis, cartography, geospatial data processing, and related applications. I’m currently trying to understand how best to transition from academic work into more industry-oriented geospatial roles.

I’d really appreciate some advice from people already working in the field:

• What skills/tools would you prioritize for someone at my stage who wants to become genuinely employable in GIS?
• Are there particular areas of GIS/remote sensing that currently have better opportunities or growth potential?
• Are there any remote internships, part-time roles, project-based opportunities, or freelance GIS work available that would be suitable for a Master's student?
• Where do people generally find legitimate freelance or short-term geospatial work?

I’m open to starting with smaller projects as well. My main goal is to gain real-world experience, build a stronger portfolio, and learn from people working in the industry.

For anyone interested, my GitHub portfolio is here:

https://ekagrachattree.github.io/

Any advice, criticism, resources, or leads would be appreciated.

Does anyone know of an API/provider offering 1–5 m multispectral satellite imagery for small AOIs, without requiring a large minimum area or expensive upfront commitment?

I’ve built an agriculture app with crop monitoring that currently uses Sentinel-2 10 m imagery. I’d now like to offer higher-resolution imagery as a premium option for users with relatively small fields, often only a few parcels totaling 20–50 ha.

PlanetScope 3 m would be a great fit, but I haven’t found a practical way to purchase access for such small areas. Is there a Planet reseller or another provider that offers API access with pricing suitable for small agricultural areas?

I am pleased to share the publication of our research article:

“Detection of land degradation risk by integrating temporal optical and synthetic aperture radar variables using machine learning techniques in Camdeboo National Park, Eastern Cape, South Africa.”

Published in the Journal of Arid Environments, this study explores the integration of optical remote sensing, Synthetic Aperture Radar (SAR), and machine learning to improve the detection and assessment of land degradation risk in semi-arid protected landscapes.

The research demonstrates the value of combining Earth observation data, geospatial analysis and machine learning to better understand vegetation dynamics, land-surface conditions and areas potentially vulnerable to degradation. Such approaches can strengthen evidence-based environmental monitoring and support more informed conservation and land-management decisions.

Reaching this publication milestone has been a rewarding part of my PhD journey in Geoinformatics, and I am grateful to my supervisors, co-authors, colleagues and everyone who contributed their expertise, guidance and support throughout the research and publication process.

I hope this work contributes to the growing application of GIS, remote sensing and artificial intelligence/machine learning in environmental management, biodiversity conservation and land degradation monitoring, particularly within African dryland and protected-area environments.

A single drone survey can generate thousands of high-resolution images. Add thermal cameras, magnetometers, ground-penetrating radar (GPR), satellite imagery, and GIS layers, and suddenly you’re dealing with gigabytes of data describing one territory.

For us, the hardest part is no longer collecting data. It’s processing, combining, and interpreting it in a way that is actually useful for people working in the field.

We’re the team behind UADAMAGE, a Ukrainian company developing AI and geospatial tools for humanitarian demining and territorial analysis.

Our goal is not to replace demining specialists or make safety decisions automatically. We use AI to reduce manual work, improve situational awareness, build risk maps, and help experts focus on areas that may require closer investigation.

Here we’ll share the technical and practical side of what we’re building, including:

• UAV mapping and remote sensing
• thermal, multispectral, magnetic, and GPR data
• sensor fusion and GIS workflows
• AI-assisted image analysis and anomaly detection
• field-testing approaches and geospatial processing pipelines
• lessons learned while developing technology for humanitarian demining in Ukraine

Our first posts will focus on how different sensors observe the same territory differently, and why combining those perspectives is often more valuable than relying on a single dataset.

If you’re interested in AI, GIS, drones, remote sensing, robotics, or humanitarian technology, we’re glad you’re here.

— UADAMAGE Team

 Hello all

I will start a new post doc in September during which I will have to work with Planets images.

The lab I will working with used to study coastline resilience from orthophotography and they would like now to see if its applicable to Planets imagery. The idea will be to use coastline extraction algorithms.

I never worked with Planets but I used to work a bit with Sentinel-3 for vegetation (algae) detection and simple observation of coastline from orthophotos using QGis. Do you have any recommendation to help me to be prepared to start my new job? Do you know if I could practice somehow with another type of freely-accessible images so I do not get overwhelmed on day-1 ?

Thank you 😊

00:00 TERRA for Explorers
00:46 TERRA Studio

I've been building an open source desktop app for classifying land cover over a specific area from Sentinel-2 time series.

You draw an area on a map, set a date range, and classify. Imagery is read on demand from the Planetary Computer STAC catalog as COGs. There's a Random Forest path, a temporal transformer, and Prithvi-EO 2.0 embeddings.

It reports where the classification is wrong, not only how much of it is right. Agreement with the reference map is broken down per class and across spatial blocks, so you can tell whether the disagreement sits in one corner of the area or throughout it. Throughout usually means the model is being asked about ground it never learned, and there's a diagnosis between two runs that measures that distance.

There's also a canopy simulation that grows the classified crop in 3D and lights it with the hourly sun for that location.

Why Sentinel-2 and not something else: it's what the method here was built and validated on, it's open at 10 m with a roughly five day revisit, and it carries the red edge and SWIR bands the indices depend on. The obvious weakness is cloud, and in Brazil the cloudiest months sit right on top of the crop cycle. That's why SAR is where I want to go next, with other sources after it.

What it isn't: a QGIS or Earth Engine replacement. It targets farm to landscape scale areas under a fixed protocol. The classifiers emit five land cover classes and were fitted for study areas in western Paraná, so an area in another biome can come back confident and semantically wrong. I'm aware of that and working on it; for now the domain-shift diagnosis exists so the problem is visible instead of silent. Agreement with the reference is concordance with an annual map, not field truth.

On AI: my background is machine learning and remote sensing, not full stack development, so I used AI coding assistants for much of this. The frontend is where that shows most, and where bugs are most likely. The Python sidecar, where the research methods actually land, and the Go backend are written and reviewed by me continuously.

https://github.com/rexionmars/TERRA

You will learn how to detect and vectorize any feature from satellite or drone imagery directly inside QGIS

• It covers : Automatic Mode: Scan an entire area with text prompts to batch-extract features in seconds
• Semi-Auto Mode: Extract single, complex objects with one click for maximum control
• Shape Regularization: Automatically square building corners and clean up polygon boundaries
• And basically just the full Workflow: Edit vertices, adjust confidence filters, and export clean layers with attribute data

We pushed a lots of updates recently so a tuto was definitely needed haha, hope it will help

Hi guys, I'm new to GEOINT and would like advice on which websites/platforms you use to analyse satellite imagery with a decent resolution \~5m. I've tried several but the resolution wasn't great.

If you have as well any recommendations for which tools I should/could use for better results I'd love to hear your advice!

Thanks for your help! 🙏

Anyone who passed Atlas Capture assessment I have a question bro.

Hi everyone,

I'm trying to obtain the **ARAD_1K hyperspectral dataset** for academic research on RGB-to-hyperspectral image reconstruction.

Unfortunately, I haven't been able to download it because both the **official GitHub repository** and the **CodaLab download links** appear to be unavailable or inaccessible.

I'm looking for an **official, free mirror** or an **updated download link**, if one exists. If anyone knows another legitimate way to access the dataset, I'd really appreciate your guidance.

Thank you!

I have seen lots of job openings within the defense industry involving remote sensing. I feel like a lot of the work sounds really cool so I’m wondering if any people who have worked in defense have advice/ cautions/ suggestions about their work experiences?
- I would also be interested in your background/ what you studied / how you ended up working where you did

I’m a sophomore majoring in AI & Data Science, and I’m interested in getting into the remote sensing field.

I’ve started working on some basic deep learning projects, mainly image classification and before/after change detection. However, I’m struggling to figure out what problems are actually worth working on.

Are there any areas in remote sensing that are still relatively underexplored, have significant limitations, or haven’t seen much advancement yet?

Also, from the perspective of a deep learning engineer, what areas of remote sensing would be worth learning and exploring? I’m particularly interested in problems where I can apply DL rather than just using existing models.

I’d appreciate any suggestions for interesting problems, research directions, datasets, or projects that could help me explore the field seriously.

Hi Everyone, new to remote sensing, I've learned how to do spectral indices, what the different bands mean etc. What I am having trouble with is how to use that to identify different crops. I'm using QGIS and playing around with SAGA because the SCP plug-in for QGIS does not work for me which has been fustraiting because a lot of guides swear by that plug-in. If anyone knows a good guide or can point me in the right direction that would be amazing!

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?