Ravindra Tripathi presents Multi-Sensor Ecological Monitoring and Spatiotemporal Habitat Analysis
On 2025-11-04 11:00:00 at G205, Karlovo náměstí 13, Praha 2
In recent years I implemented an integrated multi-sensor framework combining
cloud-based satellite analysis and UAV-derived imagery with deep learning for
high-resolution riparian habitat mapping and species monitoring Convolutional
neural networks and transformer-based detectors were trained on large annotated
datasets to enable frame-synchronized, real-time object detection at 32 frames
per second. The best detectors achieved precision exceeding 92% for swamp deer,
97% for gharial, 92% for freshwater turtles, and high precision for multiple
waterbird species. Multispectral machine-learning classification produced
habitat maps with overall accuracy of 98% (κ = 0.97), discriminating intact
grassland, degraded habitat, recently converted agriculture, and areas under
active conversion. I developed spatiotemporal models to reconstruct GPS-derived
animal trajectories, perform multi-horizon forecasts, compare model
architectures, and estimate feature importance. Analyses revealed strong links
between elevation and movement and enabled interpretable pattern discovery
related to foraging and water-seeking behaviour. Methodological contributions
include optimized UAV flight parameters to maximize detection while minimizing
disturbance, scalable annotation and augmentation pipelines, and hybrid
CNN–Transformer model designs for robustness under variable environmental
conditions. These tools supported accurate population estimation, adaptive
habitat management, and evidence-based conservation planning. Results were
published in peer-reviewed venues and all models and code are openly released.
(Ravindra Tripathi is applying for a postdoc position.)
cloud-based satellite analysis and UAV-derived imagery with deep learning for
high-resolution riparian habitat mapping and species monitoring Convolutional
neural networks and transformer-based detectors were trained on large annotated
datasets to enable frame-synchronized, real-time object detection at 32 frames
per second. The best detectors achieved precision exceeding 92% for swamp deer,
97% for gharial, 92% for freshwater turtles, and high precision for multiple
waterbird species. Multispectral machine-learning classification produced
habitat maps with overall accuracy of 98% (κ = 0.97), discriminating intact
grassland, degraded habitat, recently converted agriculture, and areas under
active conversion. I developed spatiotemporal models to reconstruct GPS-derived
animal trajectories, perform multi-horizon forecasts, compare model
architectures, and estimate feature importance. Analyses revealed strong links
between elevation and movement and enabled interpretable pattern discovery
related to foraging and water-seeking behaviour. Methodological contributions
include optimized UAV flight parameters to maximize detection while minimizing
disturbance, scalable annotation and augmentation pipelines, and hybrid
CNN–Transformer model designs for robustness under variable environmental
conditions. These tools supported accurate population estimation, adaptive
habitat management, and evidence-based conservation planning. Results were
published in peer-reviewed venues and all models and code are openly released.
(Ravindra Tripathi is applying for a postdoc position.)