On-Orbit Real-Time Wildfire Detection Under On-Board Constraints

We present a deployed system for on-orbit wildfire detec-tion aboard OroraTech’s thermal infrared Constellation Plane 1 (OTC-P1), operating under a demanding set of joint constraints: sub-megabytemodel footprint, sub-150 ms per-batch TensorRT FP16 inference on anNVIDIA Jetson Xavier NX, and an end-to-end alert pipeline targetingunder 10 minutes from satellite overpass to fire event communication.The system operates on uncalibrated mid-wave infrared (MWIR) single-band imagery at 200 m ground sampling distance, where fires frequentlyappear as sub-pixel or single-pixel thermal anomalies under extremeclass imbalance—challenges not addressed by the contextual thermal-thresholding pipelines (MODIS, VIIRS) that currently dominate opera-tional fire monitoring.We present an empirical study of lightweight dense representation learn-ing for this regime using a proprietary nine-satellite MWIR dataset. Wecompare dense masked autoencoding (DenseMAE) and a hybrid Dense-MAE+EMA (exponential moving average) distillation variant, and eval-uate representations via linear probing and full-distribution pixel-levelaverage precision (AP) under extreme class imbalance. DenseMAE pre-training enables compact downstream models that sit on the latency–accuracy Pareto frontier: our fastest SSL-pretrained model achieves 0.640test AP and 0.69 event-level Fire-F1 with 65.34 ms TensorRT FP16 la-tency per batch (224 × 224, B=8) and a 0.52 MB engine, achieved with-out pruning or compression. The best-performing configuration reaches0.699 test AP and 0.744 Fire-F1 while remaining below 1 MB. Bothoutperform a supervised baseline (up to 0.650 AP) under comparableconstraints.