AI-Powered Satellite Data Analysis for Aerospace

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

1. Requirements analysis: define tasks, data types, quality metrics.
2. Dataset preparation: collect imagery, annotate (Label Studio, CVAT), augment.
3. Model development: architecture selection, training (PyTorch, Hugging Face), quantization (INT8/FP16).
4. Integration: STAC catalog, REST API, GIS visualization.
5. Testing on real data and validation.
6. 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

Timeline

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.

How Distribution Shift Kills CV Model Metrics in Industry

On a production line, a camera is installed to control product quality. The model is trained on 10,000 labeled images—test accuracy mAP 0.84. Deployed to production, and in the first week it misses 30% of defects. Lighting on the line changes between shifts; distribution shift nullifies the metrics. This is a classic story with computer vision in industry, where pattern recognition fails without proper drift handling.

Our engineers, with experience from 60+ computer vision projects, know how to eliminate such scenarios. We guarantee stable model performance under real conditions.

Object Detection: YOLO, RT-DETR, and Everything in Between

YOLO is the standard for real-time detection. YOLOv8 and YOLOv11 from Ultralytics are the most used versions in production: simple API, active community, built-in validation, and export to ONNX/TensorRT. For tasks with high accuracy requirements and less critical latency, RT-DETR, a transformer-based architecture without NMS, gives better mAP on COCO at comparable speed to YOLOv8l.

A typical mistake when training a detector: dataset of 8000 images, 3 classes, fine-tune YOLOv8m—F1 0.73 on validation. Look at confusion matrix—one class is almost never detected. Cause: imbalance 1:23. Solution: oversampling rare class, focal loss for objectness, augmentations (Mosaic, MixUp disabled for rare class as they "blur" it). Transfer learning is mandatory: pretrained on COCO weights reduces data requirement by 10 times. Fine-tuning on 500–2000 domain images yields a working model in 1–2 days on a single GPU.

For edge deployment: export to ONNX → TensorRT engine. YOLOv8n in TensorRT FP16 on Jetson AGX Orin gives 150+ FPS at P99 latency < 8 ms—3 times faster than ONNX Runtime without TensorRT. On server A10G: 700+ FPS for YOLOv8n in TensorRT INT8.

How Does Fine-Tuning YOLO Help in Pattern Recognition?

Suppose you need to find micro-defects on a metal surface—a task with high resolution and class imbalance. We use YOLOv8m pretrained on COCO and fine-tune on 2000 proprietary images. Apply augmentations Mosaic, MixUp, random perspective. After 200 epochs, mAP 0.5 reaches 0.93. Key techniques:

• Focal loss for the objectness head—reduces contribution of easily classified examples.
• Class-balanced sampling—equalizes representation of rare classes.
• Test Time Augmentation (TTA)—increases recall by 5–7% through averaging over flips and scales.

Get a consultation on architecture selection for your task—contact us.

Segmentation: SAM, Mask R-CNN, and Instance Segmentation

SAM (Segment Anything Model) from Meta changed the approach to segmentation. SAM 2 works with video, supports object tracking across frames—for interactive object selection by point or bbox, it's the best out-of-the-box choice. For production instance segmentation without interactive prompting, Mask R-CNN or YOLOv8-seg are used. YOLOv8-seg trains like a regular detector with additional masks, convenient in the same pipelines. Semantic segmentation (each pixel is a class) uses SegFormer, DeepLabV3+. SegFormer-B5 provides a good balance of accuracy and speed for satellite imagery or medical segmentation.

Case study: cell segmentation on microscopic images. Dataset of 400 images with manual annotation. Training Mask R-CNN on ResNet-50 backbone gave IoU 0.61—poor. Problem: objects (cells) overlap; standard NMS kills overlapping predictions. Solution: switch to cellpose (specialized architecture for biomedical tasks) + soft-NMS. IoU increased to 0.79.

OCR: When Tesseract Fails

Tesseract is a starting point for simple tasks: printed text, good lighting, straight layout. As soon as there are handwritten elements, non-standard fonts, perspective distortions, or multi-column layouts, Tesseract degrades quickly.

PaddleOCR is a production-grade solution: text block detection + recognition + structural analysis. Works out of the box for 80+ languages, including Russian. Supports tables and complex document structures. TrOCR (Microsoft) is a transformer OCR with strong results on handwritten text. For Russian handwritten text, fine-tuning is needed: the base model is trained mostly on Latin script.

What to Do When Tesseract Cannot Handle Pattern Recognition on Documents?

For tasks like "extract data from invoices/contracts/passports," we use LayoutLMv3 or Donut—these models understand document layout, not just text. Integration via Hugging Face Transformers, fine-tuning on 200–500 annotated documents. Typical pipeline:

1. Preprocessing: deskew, denoising, binarization via OpenCV.
2. Text block detection: PaddleOCR detection or CRAFT.
3. Recognition: PaddleOCR recognition or TrOCR.
4. Post-processing: normalization, validation via regex or LLM for structured fields.

For documents with fixed structure, template matching + OCR by coordinates is often more reliable than an end-to-end solution.

Face Recognition: Identification and Verification

Face recognition = detection + alignment + embedding + matching. Each stage matters.

Detection: RetinaFace or InsightFace for accurate face localization and keypoints. MTCNN is older but reliable. Embedding: ArcFace (InsightFace) is state-of-the-art for face recognition embeddings. Models iresnet50/iresnet100 pretrained on MS1MV3 (5M identities). Embedding vector 512 float32, comparison by cosine similarity. Threshold tuning: decision threshold is a critical parameter. At threshold 0.6, typical FPR on LFW benchmark is 0.001, TPR is 0.985. In production, threshold must be calibrated to the real distribution: people in masks, with changed appearance, different lighting conditions. Liveness detection is mandatory: MiniFASNet—lightweight model on CPU; FaceX-Zoo contains several pretrained liveness detectors.

Video Analytics

Video is a sequence of frames plus a temporal dimension. A naive approach—detecting on every frame—is expensive.

Tracking: ByteTrack and BoT-SORT are the standard for multi-object tracking. They work on top of any detector, adding persistent IDs to objects across frames—enabling object counting, motion tracking, velocity.

Optimization: not every frame needs processing. For static scenes, detect every 5–10 frames, with tracking in between. For event detection (person entering a zone), background subtraction (OpenCV MOG2) serves as a lightweight pre-filter before neural detection. Action recognition: SlowFast, VideoMAE for action classification. Heavy models—for production use ONNX export + TensorRT or offline processing.

How to Measure Pattern Recognition Model Quality in Production?

Quality monitoring is key to MLOps. We track:

• Prediction confidence distribution.
• Share of low-confidence predictions (indicator of OOD data).
• Drift of input images via feature distribution (embeddings from backbone).

A drop in average confidence from 0.87 to 0.71 over a week is an early signal of distribution shift. NVIDIA Triton Inference Server recommends tracking these metrics via Prometheus. Our certified engineers set up monitoring and guarantee SLA for inference quality.

Deployment of CV Models

For online inference, we use Triton Inference Server (NVIDIA)—production standard for serving CV models. Supports TensorRT, ONNX, PyTorch, dynamic batching, multiple instances. REST and gRPC API. We guarantee stable operation under load.

Edge deployment: ONNX Runtime on ARM/x86 CPU. TensorFlow Lite for mobile devices. OpenVINO for Intel CPU/GPU/VPU—gives 2–3× speedup on Intel hardware compared to ONNX Runtime. After deployment, we hand over the model with documentation and train personnel.

What Is Included in the Work

Deadlines and Cost

A prototype detector on existing data takes 1–2 weeks. Production system with optimization for target hardware takes 4–8 weeks. Full cycle including data labeling (1000–5000 images) takes 2–4 months. Cost is calculated individually for each task. Typical savings from implementing a quality control system can be significant per production line.

We have been in the market for over 5 years and completed 60+ computer vision projects. We will evaluate your project end-to-end—request a consultation to get a quote and technical proposal.