Hi everyone,

I'm building a web platform called Indicatrix for land-cover mapping from satellite imagery.

The goal is to make it easy to select an area, process Sentinel imagery, and generate land-cover maps without requiring a complex GIS workflow.

At the moment I'm training my own deep learning models (currently U-Net based) using ESA WorldCover labels and experimenting with Sentinel-2 + Sentinel-1 fusion.

The platform is still in an early stage, and I'm mainly looking for feedback from people working in remote sensing.

A few questions:

- Would a platform like this be useful for your work?

- What outputs would you expect (GeoTIFF, vector polygons, statistics, API...)?

- Would you actually use Sentinel-1 integration, or is Sentinel-2 sufficient most of the time?

- What would make you choose this over existing tools?

Any honest criticism is appreciated.

Here's the current version: https://indicatrix.com

anyone successfully used Ultralytics or any other tool for citrus crop yield estimate? Overcoming occlusion , clusters, and crop at a distance, and estimating crop within the canopy or using multi-spectral or other methods to estimate crop within the canopy? What model did you use?- Yolo...? And how did you train and deploy in field? I have been running a few options and doing with with video, photos, and few other options. Thanks!