Remote Sensing Analytics
Definition
Remote sensing analytics turns raw imagery into actionable information using algorithms for detection, classification, regression, and change analysis. Techniques include spectral indices (NDVI, NBR), supervised learning (random forests, SVMs, deep CNNs), object-based image analysis, time-series modeling, and physics-informed inversion. Pipelines manage training data, cloud masking, feature engineering, cross-validation, and uncertainty estimation. Deployments run on local clusters or cloud platforms with scalable raster frameworks. Responsible analytics document data sources, model assumptions, and accuracy metrics; they include error analyses by class and geography, not just overall scores. Outputs integrate into GIS and decision dashboards.
Application
Applications span crop yield estimation, deforestation alerts, urban growth, damage assessment after disasters, illegal mining detection, and infrastructure monitoring. Finance and insurance derive risk signals; conservation uses alerts for rapid response; governments track compliance with environmental regulations.
FAQ
How to avoid label leakage?
Ensure training labels are temporally and spatially independent of test data; avoid using derived features that include future information.
Are deep nets always better?
Not necessarily—tabular models on engineered features can outperform in some contexts with less data; benchmark multiple approaches.
How to quantify uncertainty?
Use probabilistic outputs, ensembles, and calibration; map confidence and avoid hard thresholds where stakes are high.
How to operationalize at scale?
Automate ingestion and processing, use cloud-optimized formats, and set up monitoring for data gaps and model drift.