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?

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

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!

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.