AI for Satellite Image Analysis (Remote Sensing)

We build end-to-end AI systems for satellite image analysis: land cover segmentation, object detection, and change monitoring. With Sentinel-2 generating terabytes daily, manual processing is infeasible—you need to detect ships in ports, track deforestation, or assess damage. Our stack—PyTorch remote sensing, YOLO satellite detection, DeepLabV3+, SAHI—has been proven on projects for agricultural holdings and geoservices. With 5 years of experience and 15+ completed remote sensing projects, we achieve accuracy up to 0.87 mIoU on land cover tasks. We work with Sentinel-2, Landsat, Planet, and SAR data. For data preparation, we use semi-automatic annotation with SAM and experts. If you have a similar task, get a consultation—we will help choose the approach and estimate the budget.

Problems We Solve

Geospatial data is complex: multispectral channels (up to 13 for Sentinel-2), varying resolutions (10m–60m), objects ranging from a few pixels (cars) to kilometers (fields). Without AI, you either spend weeks on manual annotation or get low accuracy. We address three key tasks:

• Semantic segmentation—pixel classification into land cover classes (forest, water, built-up). DeepLabV3+ with ResNet-101 achieves mIoU 0.84 on BigEarthNet. This is 5–7% higher than UNet, thanks to atrous convolution and spatial pyramid pooling.
• Object detection—locating small targets (ships 10–50 pixels). YOLOv8x with SAHI (Slicing Aided Hyper Inference) tiles the image with overlap, aggregates predictions, and removes duplicates via NMS. This yields AP 0.79 on DOTA.
• Change detection—identifying changes between two images from different dates. A Siamese network with shared ResNet-50 encoder and a head that concatenates feature differences and products achieves F1 0.76 on SpaceNet7.

AI-based analysis is 10x faster than manual interpretation, and our models achieve up to 7% higher accuracy than traditional methods. We provide territory monitoring AI solutions that reduce analysis time from weeks to hours.

Why DeepLabV3+ Over UNet for Land Cover?

UNet is a good baseline, but for heterogeneous landscapes its skip connections fall short: small objects are lost, large ones are blurred. DeepLabV3+ uses atrous spatial pyramid pooling (ASPP), capturing context at multiple scales simultaneously. Plus the ResNet-101 encoder is deeper—101 layers vs 23 in UNet. In practice, this yields 5–7% higher mIoU on BigEarthNet and Sen12MS datasets. We have documented this in our own benchmarks.

Case Study: AI for an Agricultural Holding

From our practice: an agricultural holding wanted to automatically delineate field boundaries and identify crop types from Sentinel-2 every 5 days. We built a pipeline:

• Download 10m channels (B02, B03, B04, B08) + 20m channels (B11, B12) via rasterio, reproject to 10m.
• Compute vegetation indices NDVI, NDWI, EVI.
• Segment with DeepLabV3+ (8 classes: cropland, forest, water, urban, etc.).
• Post-process with CRF to smooth boundaries.

Result: accuracy 0.87 mIoU, processing a 1 GB scene in 4 minutes on a V100. Source: results from testing on BigEarthNet. The project took 3 months, including annotation of 5000 patches.

Our Process

1. Analysis—study the task, data sources, and metrics.
2. Data collection and preparation—download, georeferencing, augmentation (rotations, shifts, brightness changes).
3. Annotation—if new classes are needed, semi-automatic annotation with SAM and experts.
4. Training and experiments—hyperparameter tuning, architecture search, augmentation strategies.
5. Testing—on a held-out set and on real imagery with expert evaluation.
6. Deployment—package into Triton Inference Server or ONNX Runtime, expose via FastAPI. We employ MLOps geospatial practices for continuous integration and monitoring.

What's Included in AI System Development?

• Trained model with metrics (IoU, AP, F1).
• Preprocessing and inference code (Python, ready for integration).
• REST API for predictions.
• Documentation (model card, pipeline diagram).
• Operator training on the system.
• Accuracy guarantee: if after deployment metrics drop on new data, we retrain the model.

Estimated Timelines and Pricing

Pricing is determined individually after analyzing the task. Project costs start at $15,000 and can save up to 60% in operational costs. Typical investment: $15,000–$50,000. We will evaluate your project free of charge—contact us.

Public Datasets and Benchmarks

Learn more about remote sensing tasks on Wikipedia.