Our EO Techniques

UHR Image Super-Resolution

SAIIL offers state-of-the-art image super-resolution solutions for Ultra-High-Resolution satellite imagery from platforms including WorldView-3, SkySat, Deimos-2, Sentinel-2, and various smallsat constellations. Our advanced deep-learning models, leveraging cutting-edge architectures like vision transformers and generative adversarial networks, enable substantial improvements in spatial detail, texture clarity, and noise reduction. Pre-trained with extensive satellite imagery datasets, we ensure exceptional fidelity, enabling precise feature recognition, critical for urban mapping, infrastructure monitoring, precision agriculture, and disaster response.

TIR Resolution Enhancement

Our specialised super-resolution solutions for TIR, MWIR, and SWIR imagery significantly enhance the spatial resolution of thermal datasets from platforms such as Landsat, ECOSTRESS, MODIS, and ASTER. Using advanced training strategies optimised for thermal signatures, we recover critical thermal features obscured by low resolution data. Enhanced TIR/MWIR/SWIR data from our solutions empowers users to conduct detailed analyses in environmental monitoring, precise agriculture mapping, wildfire detection, and energy infrastructure management, ensuring robust operational decision-making even in challenging thermal environments.

Building & Road Detection

We provide advanced automated detection and classification of buildings and road networks using state-of-the-art vision transformer models that accurately extract detailed infrastructure information from high-resolution satellite and aerial imagery. Our deep learning solutions enable precise mapping and monitoring of urban growth, infrastructure planning, post-disaster assessment, and navigation system improvements. By training custom AI models on extensive EO datasets, we ensure exceptional performance across diverse urban and rural landscapes, significantly reducing manual analysis effort and accelerating operational workflows. We offer tailored deployments, AI model optimisation, and ongoing technical support, enabling seamless integration into your geospatial intelligence and urban analytics projects.

Solar Panel Detection

We also provide specialised retrained AI models for solar panel identification from high-resolution satellite and UAV imagery, to support accurate mapping, monitoring, and assessment of renewable energy installations. With precise localisation and real-time detection performance, our solar panel detection capability significantly enhances efficiency in renewable energy surveys, urban planning, inspection and monitoring applications.

Cloud Motion Tracking

SAIIL provides state-of-the-art cloud tracking and cloud motion vector analysis through advanced deep-learning-based scene flow techniques. Our methods precisely track cloud movements, delivering accurate sub-pixel motion vectors essential for weather forecasting, climate research, and renewable energy applications. Our models are specifically trained and fine-tuned using large-scale, high-resolution atmospheric imagery, enabling reliable cloud and wind motion predictions even in challenging meteorological conditions.

Change & Anomaly Detection

SAIIL’s deep-learning-driven building change detection solutions enable rapid and accurate identification of structural changes over time. Using spatial-temporal vision transformer architectures, we precisely identify subtle alterations, additions, or demolitions in urban landscapes. Our tailored training on extensive multi-temporal datasets ensures robustness and accuracy, providing crucial intelligence for urban planning, infrastructure assessments, and rapid post-disaster evaluation. In addition to buildings, our change and anomaly detection methods can be applied to identify road network updates, landslides, wildfire burn scars, and other surface changes, supporting a wide range of environmental and infrastructure monitoring applications.

EO Scene Classification

SAIIL’s scene classification solutions utilise efficient and robust deep-learning architectures to categorise satellite imagery into meaningful land-use classes accurately and swiftly. Our custom-trained models on specialised EO datasets like Sentinel-2 (EuroSAT) and USGS National Map Urban Area Imagery offer superior accuracy in identifying terrain types, land cover, and environmental contexts, greatly aiding in land management, agricultural monitoring, environmental assessment, and strategic urban development planning. These solutions provide users with actionable insights quickly and reliably, ensuring informed decisions across diverse EO applications.

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