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Computer Vision
Satellite Images Processing
View from above. Applied intelligence.
Satellites generate unprecedented amounts of visual data every day: multispectral, radar, high-resolution optical images covering territories, infrastructure, crops, oceans. Data that exists, is accessible and contains valuable information — but in most cases is not analyzed systematically.
Our team has direct experience developing satellite image analysis pipelines: from raw data management to actionable output production.
Problems we solve
Blocked knowledge, critical dependencies and inefficiencies holding back growth
Satellite data available but not analyzed
Sentinel, Landsat, Planet, radar data: accessible sources that most organizations do not know how to process systematically.
Manual monitoring of large territories
Visually inspecting large geographic areas with dedicated staff does not scale. An AI system does it automatically.
Detecting changes over time
Identifying what has changed between two acquisitions requires models specific to multitemporal comparison.
Heterogeneity of satellite sources
Optical, multispectral, SAR sensors: each source has its own characteristics and preprocessing logic.
Use Cases
Real projects, measurable results
01
Multitemporal analysis of satellite images for automatic detection of territorial changes: land cov…
Change detection on time series
Multitemporal analysis of satellite images for automatic detection of territorial changes: land cover variations, urban expansion, deforestation, infrastructure modifications.
From satellite to change report, automatically and traceably.
02
Semantic segmentation of satellite images for automatic mapping of land use: urban, agricultural, f…
Territory classification and land use mapping
Semantic segmentation of satellite images for automatic mapping of land use: urban, agricultural, forest, water, industrial areas.
Automatically updated thematic maps, without manual analysis.
03
Processing of multispectral images to calculate vegetation indices (NDVI and derivatives), monitori…
Agricultural analysis and vegetation
Processing of multispectral images to calculate vegetation indices (NDVI and derivatives), monitoring crop status, detecting water stress and estimating agricultural productivity.
Agronomic data at territorial scale, without field intervention.
Technology Corner
The architecture and technologies that make the difference
Our projects don't rely on off-the-shelf solutions. Every system is designed on the right architecture for the specific problem — and the technology choice makes the difference between a tool that works in demo and one that holds up in production.
Preprocessing and satellite data management
- ✓ Atmospheric and radiometric correctionnormalization of optical images
- ✓ SAR data preprocessingspeckle filtering, geocoding, calibration
- ✓ Fusion of heterogeneous sourcesoptical, multispectral, radar, hyperspectral
Computer vision models for remote sensing
- ✓ Semantic and panoptic segmentationterritory classification
- ✓ Change detectionsiamese architectures and multitemporal difference models
- ✓ Pre-trained models on remote sensing datasetsBigEarthNet, SpaceNet, DOTA with domain-specific fine-tuning
Georeferencing and GIS integration
- ✓ Georeferenced outputGeoTIFF, Shapefile, GeoJSON
- ✓ Integration with GIS platformsQGIS, ArcGIS, Google Earth Engine
- ✓ Spectral indicesNDVI, NDWI, EVI, SAVI and derivatives
Interested in this service?
Contact us for a free consultation and find out how we can help you.
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