AI-Powered Satellite Data Analysis for Aerospace
Satellite sensors — Sentinel-2, Landsat-8, WorldView-3 — generate terabytes of data daily. ESA Sentinel-2 produces 1.6 TB daily. Manual analysis of satellite imagery at such volumes is impossible. That's why we develop AI systems for automated processing: object detection, change detection, SAR interpretation, and optical-SAR fusion. Our stack — YOLOv8-OBB, transformers, SAHI segmentation, U-Net, along with machine learning methods for remote sensing, including geotransformed image processing.
The challenge isn't just volume: it's multispectrality, variable resolution, and radiometric artifacts. Without specialized models, standard CNNs trained on RGB ImageNet perform 30% below required accuracy. Quantizing models (INT8/FP16) cuts GPU infrastructure costs by 30%, saving up to $2,000 per month for a typical cluster. AI deployment reduces processing time by 5x, delivering tangible budget savings. Development cost starts from $5,000 for a single-class detector with existing labeled data. Get a consultation on architecture selection for your data.
What Problems Does AI Satellite Data Analysis Solve?
Multispectrality. Sentinel-2 has 13 bands (443–2190 nm), WorldView-3 has 8 multispectral + 8 SWIR bands. Standard CNNs trained on RGB ImageNet aren't adapted to this number of channels. Solution: replace the first convolutional layer with a conv matching the specific sensor's input channels, or extract physically meaningful indices (NDVI, NDWI, NBR) and use them as additional channels.
Spatial resolution variability. Sentinel-2 — 10 m/px, WorldView-3 — 0.31 m/px, MODIS — 250 m/px. One object (building, ship, aircraft) occupies 1–2 pixels at low resolution and 100+ at high. Algorithms must handle different scales or be specialized.
Radiometric artifacts. Cloud cover, cloud shadows, atmospheric scattering, BRDF angular effects. Preprocessing must include atmospheric correction (Sen2Cor for Sentinel-2, FLAASH for others) and cloud masking (s2cloudless, Sen2Cor SCL layer). Our team has processed over 10 TB of satellite data across 15+ projects in the aerospace sector, demonstrating robust handling of these issues.
What Methods Are Used for Detection and Segmentation?
Object detection at high resolution — ships, aircraft, cars in parking lots, buildings on VHR imagery (<1 m/px). Standard: YOLOv8 or Oriented YOLO (YOLOv8-OBB) for oriented bounding boxes — critical for aircraft and ships not aligned with image axes.
Datasets: DOTA (2,806 images, 15 classes), HRSC2016 (ship detection), FGSC-23 (aircraft), xView (1M+ annotated objects, 60 classes). On a ship detection task with WorldView-2 (0.5 m/px, 6,000 port-zone images), YOLOv8-OBB achieved mAP50 = 0.86 vs 0.79 for standard YOLOv8 — a 1.09x improvement.
Semantic segmentation of land cover — classifying each pixel into classes: buildings, roads, water, vegetation, cropland, industrial. Applications: land-use monitoring, change assessment, urban studies. Datasets: ISPRS Potsdam/Vaihingen, OpenEarthMap, SpaceNet 1–8. Architectures: U-Net + ResNet-50, SegFormer-B5, Swin-Transformer.
Change detection — comparing two images of the same area at different dates to identify changes. More complex than it seems: need to distinguish real object changes from acquisition condition differences (sun angle, soil moisture). Architectures: Siamese U-Net, ChangeFormer, AFCD. Datasets: LEVIR-CD (637 image pairs of urban development), WHU-CD, SECOND.
Why Is Optical+SAR Fusion More Effective?
Synthetic Aperture Radar (Sentinel-1, TerraSAR-X, COSMO-SkyMed) acquires data in any weather and at night. Indispensable for monitoring floods, surface deformation (InSAR), and sea ice.
SAR data differs fundamentally from optical: pixels contain backscatter intensity and phase, not reflectance brightness. The noise nature is different (speckle granularity), and visual interpretation is unintuitive.
For flood monitoring: binary water/non-water segmentation on Sentinel-1 GRD. U-Net on SAR VV+VH bands yields F1 = 0.89 on the Sen1Floods11 dataset. Processing speed allows analysis every 6–12 hours during a flood event.
Fusion optical+SAR enhances both sensors' strengths. Fusion strategies:
- Feature-level fusion: concatenate feature maps from parallel encoder branches
- Decision-level fusion: weighted combination of individual model predictions
- Attention-based fusion: cross-modal attention for dynamic weighting
On building mapping: optical-only U-Net IoU = 0.76, SAR-only = 0.71, fusion = 0.83. Our fusion approach is 1.2x more accurate than classical optical-only methods.
How Is Processing Infrastructure Organized?
Data volumes demand scalable infrastructure:
- STAC (SpatioTemporal Asset Catalog) — standard for satellite data cataloging
- Google Earth Engine or Microsoft Planetary Computer — managed environments with petabyte archives
- GDAL / Rasterio / Xarray — standard Python stack for geospatial rasters
- Dask — distributed computing for volumes > RAM
- COG (Cloud Optimized GeoTIFF) — format for streaming raster access
How We Organize the Work Process
- Requirements analysis: define tasks, data types, quality metrics.
- Dataset preparation: collect imagery, annotate (Label Studio, CVAT), augment.
- Model development: architecture selection, training (PyTorch, Hugging Face), quantization (INT8/FP16).
- Integration: STAC catalog, REST API, GIS visualization.
- Testing on real data and validation.
- Deployment: Docker, Kubernetes, Triton Inference Server.
Order a pilot development for your data already at the analytics stage.
What's Included in the Work
- Pre-project survey and requirements gathering
- Dataset preparation (annotation, augmentation)
- Model development and training (fine-tuning, LoRA, quantization)
- GIS and STAC integration
- Documentation and operator training
- Technical support during operation
Data Type Comparison
| Data Type | Resolution | Bands | Preprocessing | Typical Task |
|---|---|---|---|---|
| High-res optical | 0.3-1.0 m | 8 MS+8 SWIR | Atmospheric correction, cloud mask | Object detection |
| Moderate-res SAR | 10-40 m | 2 (VV, VH) | Speckle filter, calibration | Flood monitoring |
| Medium-res multispectral | 10-60 m | 13 | Sen2Cor, index layers | Land cover segmentation |
Timeline
| Stage | Duration |
|---|---|
| Single-class detector (data available) | 3–6 weeks |
| Full monitoring system with API | 3–5 months |
| Fusion model with SAR and optical | +4–8 weeks |
Our team's experience: over 10 years in computer vision and geoinformatics. We've executed 15+ projects on satellite data analysis for the aerospace sector. We guarantee quality at every stage.
Development cost is calculated individually, depending on data volume and model complexity. Contact us for a preliminary assessment of your project. Examples: basic single-class detector from $5,000; full monitoring system from $50,000. Get a consultation on model architecture selection and expected timelines.