Production-validated models with measurable accuracy benchmarks on real Earth observation datasets.
Optimized ONNX and TensorRT models delivering sub-second predictions for live data streams.
Unified models trained on optical, SAR, LiDAR, and multispectral data across sensor types.
End-to-end automation from data labeling to model monitoring with MLflow, Kubeflow, and CI/CD.
What We Build
Production-grade deep learning models built, trained, and deployed for Earth observation and geospatial analysis.
Object Detection
State-of-the-art YOLOv8 and Faster R-CNN for buildings, vehicles, ships, and aircraft identification with 92%+ mAP accuracy on satellite imagery.
Semantic Segmentation
U-Net and DeepLabv3+ for pixel-level LULC mapping, building footprint extraction, and road network delineation with IoU scores above 85%.
Change Detection
Siamese networks and ChangeFormer for bi-temporal analysis detecting construction, deforestation, urban expansion, and disaster damage.
Feature Extraction
CNN-based extraction of coastlines, water bodies, and roads with graph neural networks for topology preservation and OSM integration.
Image Enhancement
ESRGAN super-resolution for 4x–8x upscaling, GAN-based cloud removal, and atmospheric correction for consistent surface reflectance.
Automated MLOps
Kubeflow and MLflow pipelines for continuous monitoring, model versioning, drift detection, and real-time inference APIs.
AI-Powered Solutions
Real-world applications where AI transforms geospatial analysis across industries and mission types.
Infrastructure Monitoring
Mask R-CNN for road, bridge, and pipeline detection. Crack detection CNNs and thermal anomaly analysis for predictive maintenance.
Agricultural Intelligence
LSTM models for crop classification using Sentinel-2 time series. NDVI-based yield prediction and hyperspectral disease detection.
Defense & Security
Transfer learning for military asset classification. Object tracking and spatiotemporal pattern analysis for GEOINT production.
Disaster Response
xBD-trained models for building damage classification. SAR-based flood mapping and VIIRS/MODIS active fire detection.
Urban Planning
Automated building footprint extraction for population estimation. Urban growth modeling and informal settlement detection.
Environmental Monitoring
Deforestation alert systems using Sentinel-1/2 fusion. Illegal mining detection and carbon stock estimation for REDD+ MRV.
Maritime Intelligence
Ship detection and AIS correlation for dark vessel identification. Oil spill detection using SAR polarimetry and CNN classifiers.
Energy Sector
Solar potential mapping using DSM and irradiance modeling. Wind farm site selection and transmission line vegetation encroachment.
Deep Learning Architectures
We leverage, fine-tune, and extend state-of-the-art models for your specific geospatial use case.
YOLO v8
Real-time detection for vehicles, structures, and infrastructure at scale.
U-Net
Precise boundary delineation for buildings, parcels, and land cover.
DeepLabv3+
Multi-scale land cover and vegetation classification.
Faster R-CNN
High-accuracy object detection for complex scene analysis.
ResNet / EfficientNet
Scene and aerial image classification at scale.
Vision Transformers
State-of-the-art visual understanding for large-scale EO tasks.
End-to-End ML Pipeline
A systematic, production-focused process from raw imagery to deployed, monitored AI systems.
Data Preparation
Image preprocessing, tiling, augmentation, and annotation pipeline setup.
Model Development
Architecture selection, transfer learning, training, and hyperparameter tuning.
Evaluation & QA
Accuracy assessment, confusion matrix analysis, and performance benchmarking.
Deployment
ONNX/TensorRT optimization, REST API development, and production integration.
Monitoring & MLOps
Performance tracking, drift detection, retraining triggers, and model versioning.