Extracting road networks manually is tedious. Discover how to automate road centerline extraction from aerial LiDAR point clouds using intensity filtering and graph theory. Click here to read.

Extracting road networks manually is tedious. Discover how to automate road centerline extraction from aerial LiDAR point clouds using intensity filtering and graph theory. Click here to read.
Post-disaster response demands rapid mapping. Learn how to automate landslide detection using Sentinel-2 multispectral imagery and Python with NDVI differencing. Click here for the tutorial.
kick-off keynotes by Norbert Pfeifer (TU Vienna) about 3D point clouds and topographic lidar and Melanie Elias (HTW Dresden) on 4D photogrammetry and geovisualisation both targeting mountain research at #sensingmountains #remotesensing @geographyinnsbruck @uniinnsbruck
intensive scientific exchange during #sensingmountains in #obergurgl discussing and working on close-range and #remotesensing and #earthobservation in mountain regions for change detection and time series analysis identifying and characterising processes of change @geographyinnsbruck @uniinnsbruck
Just Published 📰Modelling individual tree diameter growth based on airborne laser scanning data 🍁
✒️Jääskeläinen et al. Itä-Suomen yliopisto / University of Eastern Finland
🔗https://ow.ly/TMoP50ZOPJ2
📷Fig 2: Examples of accurate and inaccurate segmentation
📢New monograph in @ecosistemas-aeet.bsky.social about #Ecology and #RemoteSensing
👉🏽Lead by @ajpelu.bsky.social and Cristina Acosta-Muñoz
Join in and contribute to the @eco-aeet.bsky.social #Diamond #OpenAccess journal and community!
More info👇🏽
New Postdoctoral Scholar Position on Lidar Remote Sensing of Marine Plankton with the Behrenfeld lab at Oregon State University to work on remote sensing retrievals of ocean plankton properties.
Apply by 15 November.
Being proud of such a great collaborative efforts of experts working to use #remotesensing to map conflict-damage and now the DDMG has been awarded the AGU Open Science Team prize. Thanks to all, in particular @jamonvdh.bsky.social and Corey Scher for leading on this. www.agu.org/user-profile...
Want to work with the AlphaEarth Embedding data from R? 🛰️🌍
Felipe Carlos has developed an R package that makes it easier to search and retrieve AlphaEarth embeddings for your area of interest.
Want to work with the AlphaEarth Embedding data from R? 🛰️🌍
Felipe Carlos has developed an R package that makes it easier to search and retrieve AlphaEarth embeddings for your area of interest.
Point ENVI at the metadata file, not the image files.
Open the Sentinel-2 bundle through its metadata and ENVI builds the band stacks for you: the 10 m bands, the 20 m bands and the 60 m bands. Then pick any three for a colour composite.
Every year, at least one person gets these the wrong way round and wonders why their study area is in the Indian Ocean.
In Google Earth Engine, a point is longitude first, then latitude.
Twenty minutes of pointing and clicking found three satellite scenes.
Every Sentinel-2 scene over this area for the last five years? I'm not clicking through that. I'm certainly not downloading it.
The choice between a browser and a STAC catalogue is really a choice between working on one scene and working on a thousand.
LC09_L2SP_030027_20260723_20260725_02_T1 is not noise.
Landsat 9. Level-2 science product. WRS path 30, row 27. Acquired, processed, Collection 2, Tier 1.
Download Level 1 when you needed Level 2 and it costs you an afternoon and several gigabytes.
Check the processing level before you click download. Every single time.
Every remote sensing project starts with the same two questions. Where do I get the data? And what do I do with the file once I have it?
🌍🛰️ Join the free Topics Webinar on 14 Nov 2026: “Satellite-Based LULC Change & Biodiversity Monitoring in India: From Earth Observation to Policy Action.” Explore how EO supports conservation, climate resilience & policy.
👉 Register: brnw.ch/21x5SfV
I am looking to hire a postdoc at Pacific Northwest National Laboratory.
The focus will be on improving the representation of dynamic urban evolution in DOE's Energy Exascale #EarthSystemModel by combining #satellite #remotesensing and #machinelearning.
More details: careers.pnnl.gov/jobs/12145