KappaMaskv2: Going global
We are happy to anounce that KappaMaskv2 is finally released. KappaMaskv2 can detect clouds all over the globe in any season. It was trained on KappaSet, a large and diverse cloud and cloud shadow dataset developed with the help of active learning. Try KappaMaskv2 out here: https://github.com/kappazeta/km_predict. You can get KappaSet here: https://zenodo.org/record/7100327. Share your experinece with us!
We are happy to anounce that KappaMaskv2 is finally released. KappaMaskv2 can detect clouds all over the globe in any season. It was trained on KappaSet, a large and diverse cloud and cloud shadow dataset developed with the help of active learning. Try KappaMaskv2 out here: https://github.com/kappazeta/km_predict. You can get KappaSet here: https://zenodo.org/record/7100327. Share your experinece with us!
Handling speckle noise on SAR images
Synthetic aperture radar (SAR) systems offer high-resolution images, which, however, suffer from strong fluctuations due to the speckle phenomenon, customarily referred to as noise. The design of efficient despeckling filters is a long-standing problem that has been the object of intense research since the advent of SAR technology. In our new blog post, we take a look at different despeckling methods.
Synthetic aperture radar (SAR) systems offer high-resolution images, which, however, suffer from strong fluctuations due to the speckle phenomenon, customarily referred to as noise. The design of efficient despeckling filters is a long-standing problem that has been the object of intense research since the advent of SAR technology. In our new blog post, we take a look at different despeckling methods.
Why do we need Sentinel-1 data service?
The Sentinel-1 data is different than most other satellite data because it is not dependent on weather or daylight, and thus is able to provide data on days where other satellites cannot because of clouds. We have a new blog post by Olga Wold, our geospatial data quality specialist.
The Sentinel-1 data is different than most other satellite data because it is not dependent on weather or daylight, and thus is able to provide data on days where other satellites cannot because of clouds. We have a new blog post by Olga Wold, our geospatial data quality specialist.
Advanced speckle filtering
Our raster processing got a major upgrade that significantly improves the quality of our backscatter and coherence rasters. We analyzed, modified and combined multiple published methods when designing a new filter for KappaOne.
Our raster processing got a major upgrade that significantly improves the quality of our backscatter and coherence rasters. We analyzed, modified and combined multiple published methods when designing a new filter for KappaOne.
KappaZeta is making Sentinel-1 data very easy to use
Copernicus Sentinel satellites produce several terabytes of data every day. For many users the data is complex to understand and use. KappaZeta is addressing this problem for Sentinel-1 with pre-processing and providing the derived layers in an analysis-ready format. The goal is to have a service, where obtaining the right satellite image takes either a single mouse click or an API command. The vision will be implemented under the InCubed programme of European Space Agency.
Copernicus Sentinel satellites produce several terabytes of data every day. For many users the data is complex to understand and use. KappaZeta is addressing this problem for Sentinel-1 with pre-processing and providing the derived layers in an analysis-ready format. The goal is to have a service, where obtaining the right satellite image takes either a single mouse click or an API command. The vision will be implemented under the InCubed programme of European Space Agency.
Our crop monitoring service prototype is live
In the beginning of May we launched our crop monitoring prototype service. It is the first phase of the Harvesting Time Recommendation service prototype and allows the farmers to see the ongoing season timeseries of Sentinel-1 and Sentinel-2 field-based features and NDVI/RGB/NRG images which are being updated as soon as new images are available.
In the beginning of May we launched our crop monitoring prototype service. It is the first phase of the Harvesting Time Recommendation service prototype and allows the farmers to see the ongoing season timeseries of Sentinel-1 and Sentinel-2 field-based features and NDVI/RGB/NRG images which are being updated as soon as new images are available.
|
EU Subsidy Checks
Replacing on-the-spot-checks on grasslands with an automated solution |
Radar Time Series Ready to use in various machine learning algorithms |
Research and Consulting Developing new models and classifiers for different use cases |