Gopal Erinjippurath, Senior Director, Analytics Engineering, Planet Inc.
Presented at MLconf 2018
Abstract: By imaging the entirety of Earth’s landmass every day at 3.7m resolution and enabling on-demand follow up imagery at 80cm resolution, Planet offers a uniquely valuable dataset for creating datasets for imagery analytics over varied context. We introduce Planet imagery towards creating large scale datasets for object detection and localization and associated analytics. We describe workflows for data collection and aggregation and demonstrate the results of experiments with baseline state of the art deep learning based object detection models (Faster RCNN and SSD) for object detection and localization. We also describe a few early experiments with transferability of object localization between datasets from different satellite constellations. These approaches are then applied towards localizing objects of relevance towards federal disaster response and emergency management efforts. We showcase examples of our imagery, objects of relevance and detections from the baseline model in disaster regions.
See Gopal's presentation slides on our slideshare page here: [ Ссылка ]
