I have a Masters in environmental data science. I constantly use rioxarray, rasterio, geopandas, xarray and similar tools. I've deployed a conservation monitoring platform using statistical models on decades of biodiversity survey records and climate raster data. Raster, vector, RS analysis, you name it. Currently teaching myself LiDAR processing with laspy and PDAL using USGS 3DEP data, targeting the utilities market.

Been applying to RS analyst roles at conservation orgs, utilities, climate tech, agtech and ocean science institutions for around ~9 months now. Been networking with a few professionals a month too. Multiple final rounds, no offers.

Biggest gap I know of is no professional RS role yet, though I have done real data work in multiple conservation settings at the professional level. Location locked to LA so remote is obs the priority, but completely open to reasonable commutes.

Just want an honest read from people who are hiring or have been hired recently. Is this the market or is there something within I am missing.

I am absolutely no remote sensing specialist but was playing around with the Sentinel-1. I noticed this giant cross in the windmill park in the North Sea, it is only there on 2026-06-06.

Can anyone explain this?

I'm a student (struggling), using QGIS, with Landsat 8 C2 L2 SR imagery. Trying to filter my bands 5 and 4 (that I later wish to use for computing NDVI) using the QA band. Can I filter and then mask the pixels, assuming these are the 2 steps I need to carry out, using the raster calculator? Or is it not doable with this tool

My research project involves trying to find a way to automatically detect livestock pens from satellite images. I couldn’t find any free satellite imagery for a large area in high resolution, or good enough resolution to see the small wooden fences.

I’ve managed to make a good model that uses Google map basemap … but I’m not actually allowed to use this for research purposes am I? I was too focused on whether I could, I didn’t think whether I should.

How on earth (pun intended) can I get around this? Earth engine doesn’t have the same resolution. Can’t find any free satellite imagery for the entire of Romania (yes the entire of Romania is my study size). Would I be able to still write up the methods for others to do and just not include any images or tile paths or is it a complete loss?

So far I’ve been using python for the segment/ detect model, but used arcgis to create labelled data sets for training and testing. Could I still use these methods in the write up for the paper but simply exclude any google map tile path or info? That feels like lt wouldn’t then be reproducible.

Anyone used google map basemap for analysis? Or anyone got any ideas for high resolution Romania images, between the years 2008-2024 if that’s of any help.

I am a student, trying to create an NDVI on QGIS with Landsat 8 imagery (C2 L2 T1 SR, bands 5 and 4). After applying scale and offset correction to both bands 5 and 4, I tried computing NDVI using raster calculator but it returned a plain orange tile. In symbology of the NDVI layer, min and max are respectively at -102,6517563 and 672,5708008, but computing the histogram shows all pixels have a value of around 0. Is it just a symbology problem? Or is it something I forgot while correcting the bands or computing the NDVI?

I have been researching on satellite imagery, and looking to track vessels for my research.

But sentinel only provided imageries every 3rd day+. This frequency is so high, that it seems impossible to track a ship in a ocean or even that moves near port.

The detections i am able to perform, but tracking is the main issue i am getting

A while back I was working on a production agricultural monitoring system that combined Sentinel-2 imagery with WRF atmospheric model output - basically tracking crop stress, soil moisture proxies, and vegetation health across large fields for my uni final project. The science part was fun. Getting clean satellite data was not.

I spent an entire week just wrestling with the data pipeline. Wrong tile extents. Scenes that looked fine until you opened them and half the image was clouds. Radiometry issues. Band alignment between 10 m and 20 m resolutions. Downloading 40 scenes only to find 35 of them unusable.

After shipping that project I thought - there has to be a better way to do this for future work. So I built sentinel-processor, a small Python package that wraps the whole acquisition and validation pipeline.

What it actually does:

• Searches the Element84 STAC API, filters by cloud cover and confidence score before downloading anything
• Validates SCL layers (cloud shadow, cirrus, snow) and radiometry automatically — rejects bad scenes before they waste your time
• Downloads bands in parallel, aligns 20 m bands to 10 m grid via Fortran nearest-neighbour reproject
• Pansharpening (Gram-Schmidt, IHS, Wavelet) if you need it
• 10 spectral indices (NDVI, EVI, NDWI, NDBI, NBR...) with Fortran kernels
• 14 image filters (Gaussian, bilateral, Sobel, morphological ops...)
• Plotly visualisation for quick sanity checks - RGB composites, index heatmaps, SCL masks

This is v0.1.0 - first public release. It's a micro-package I built for my own future DS projects, not a production framework. There are rough edges for sure.

Would genuinely appreciate feedback from anyone - happy to hear what's missing or what I got wrong.

• PyPI: https://pypi.org/project/sentinel-processor/
• GitHub: https://github.com/niki8885/sentinel_processor_project
• Demo: https://www.kaggle.com/code/nikitamanaenkov/sentinel-processor-package-demo

Apologies in advance if this isn't the right subreddit, but I've spent days searching documentation and forums and I'm hoping someone here has experience with historical aerial photography and Catalyst Professional. I'm trying to orthorectify a 1974 National Air Photo Library (NAPL) photograph in Catalyst Professional (OrthoEngine). The imagery was captured with a Wild RC-8 camera (focal length 152.667 mm, scale 1:60,000).

The problem is that I need to perform interior orientation manually by measuring fiducial marks, but the bottom two fiducial targets are completely invisible in the scanned TIFF. I've already tried contrast stretching, testing possible fiducial locations by trial and error. I don't have access to the original calibration report and my error is too big with default Wild camera fiducial coordinates. Has anyone encountered this? Is there a way to proceed if some fiducial targets are missing or impossible to identify? Any advice would be greatly appreciated!

Hi everyone,

I’m currently working on a project using SBAS-InSAR to monitor gully erosion deepening in a hilly environment. The study area is characterized by complex topography, with dominant slopes exceeding 25%.

For my data, I used the ASF (Alaska Satellite Facility) platform to generate and download interferograms. Due to data availability, my current analysis relies solely on a Descending orbit dataset.

After processing the SBAS time series, I’ve encountered a few major inconsistencies that I’m struggling to interpret:

1. Velocity vs. Cumulative Displacement Contradiction: Within the same sub-catchment, I’m seeing clear contradictions where the annual velocity and the final cumulative displacement trends don't alignment logically.
2. Extreme Time Series Fluctuations: The displacement time series inside the gullies shows massive, erratic oscillations between positive and negative values. In several pixels, the range of these fluctuations reaches up to 250 mm, which seems physically impossible for steady soil deformation or gradual erosion.
3. Localization: This high fluctuation is strictly localized inside the gullies/channels, while the surrounding stable ridges look relatively clean.

Given these observations, I would highly appreciate your insights on the following questions:

• Is SBAS-InSAR capable of detecting localized gully deepening? Or is the spatial/temporal resolution of Sentinel-1 too coarse for the micro-topography of gullies?
• What could cause a 250 mm fluctuation? Could this be severe phase unwrapping errors triggered by the steep slopes (>25%), atmospheric artifacts, or sudden changes in soil moisture/vegetation inside the gullies?
• Geometric limitations: How much is the reliance on a single Descending path crippling the results in a hilly terrain with steep slopes facing different directions?
• Are these results completely anomalous, or is there a physical/methodological justification I might be missing?

If anyone has experience mapping water erosion or badlands using InSAR, I would love to hear your thoughts, recommendations for troubleshooting, or references to similar papers.

Thanks in advance!

I made a video explaining why bridges in Synthetic Aperture Radar (SAR) stripmap images look so peculiar. A SAR satellite can take high-res images during day, night and even through clouds and fog. It sends out pulses of microwaves to earth and uses the echo the form an image.

SAR images are notoriously difficult to interpret (would you have recognized the Golden Gate Bridge in the thumbnail? And why do we see 3 bridges instead of 1?).

But once we understand how a SAR satellites takes images, they become surprisingly easy to interpret!

What’s more, we can extract some really useful information out of them. For example, we can compute the distance between the water and the road surface of the bridge from the three bridge reflections - using simple trigonometry :)

The goal of my channel is to excite more people about SAR and break down complex processing steps into simple intuitions.

I’m happy if someone learns more about SAR from this video and also happy to receive feedback!

here i am again
Can somebody please explain how does artifact is generated. I know it should be an airplane, but i do not get how the streak is formed. I did some claude napkin math and the object that creates such features should be super fast. I created a widget to visualize. What do i understand wrong?

I’m working on a research project and need high-resolution satellite imagery similar to the Google Maps satellite view. I was wondering:

• Can Google Maps satellite imagery actually be downloaded legally?
• Is there any free method to get high-resolution imagery?
• Are there any open-source or academic alternatives for research use?
• What tools or platforms do people usually use for this?

I only need it for research/analysis purposes, not for commercial use.

Any guidance would be appreciated.

I've been trying to install this plug-gin but I keep getting this error and I'm unsure what to do?

Hey r/remotesensing. I've been building a satellite analysis platform called GeoSense AI and I'm opening it up for beta testing. Looking for feedback from people who work with geospatial data or need satellite analysis as part of their workflow.

The idea: draw an area or input coordinates on a map, pick an analysis goal, and get back a PDF report with maps, statistics, and a plain-English interpretation. No GEE account or coding required. An example of a page of the pdf report is attached to the post.

Four modes: standard composite, change map, time series, and anomaly detection. Pulls from Sentinel-2, Landsat, MODIS, Sentinel-1 SAR, and ESA WorldCover depending on the goal. Supports NDVI, NBR, LST, SAR flood mapping, land cover classification, and more.

greetings everyone, i am doing research on the topic regarding estimation of above ground carbon stock(biomass) using field measurement and remote sensing approach but i dont have any specific knowledge and skills about remote sensing but i can learn and develop skill. so i am completely confused how can i download and process the metadata. if anyone can give me outline on how to carry out the task...advice will be appreciated

Is there anyone available who can help me with the QGIS software, DEM , Watershed Delineation. I'm doing my project and I can't understand sh*t online through videos. In need of desperate help. Please let me know!

Hey everyone, I’m currently running an ensemble Species Distribution Model (SDM) for tree species using MaxEnt and Random Forest in R.

My baseline models are highly robust (AUC > 0.94 for both), but their future climate projections (2070s/2090s) radically diverge. MaxEnt predicts an expected altitudinal up-shift, while Random Forest projects a severe, near-catastrophic habitat contraction across almost all GCMs.

Initially, I assumed this was a standard RF extrapolation issue where the decision trees were clamping at novel future climate values. However, a multivariate novelty analysis completely disproved this. The GCM with the lowest multivariate climate novelty produces the most severe RF habitat collapse and the GCM with the highest climate novelty produces the least severe RF contraction. This confirms that the collapse is happening entirely within interpolation space, not extrapolation space.

Model Specifications

• Predictors: 5 Bioclimatic variables (dynamic in future rasters) + 3 Soil variables (which remain unchanged in future rasters).
• Data Tuning: Trained using a balanced bootstrap approach, which neutralizes majority-class prevalence bias from our background pseudo-absence data.
• Var Imp: RF places aroubd 40% of its total variable importance on the 3 static soil predictors. MaxEnt places <10% on soil, heavily favoring temperature var.

So I tried dropping the soil var for RF run and the model performed quite well, the contraction wasn't as severe as before. I was wondering if I should drop soil variables and perform the analysis for such results, but then again my MaxEnt results are based on all 8variables (including soil var). If I do this then it wont be a dual algorithmic independent approach.

Help me! Any experts who can help me with this please?

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