AwakeForest: An Interactive Geospatial Platform for Large-Scale Forest Imagery
This work provides a unified, scalable tool for practitioners needing geospatial-native, cloud-optimized, and ML-integrated workflows for forest imagery analysis, addressing a lack of such integrated platforms.
AwakeForest is an interactive geospatial platform for large-scale forest imagery that integrates model-assisted inference, automatic annotation, and human-in-the-loop refinement. It supports plug-and-play pretrained models and handles gigabyte-scale orthomosaics, demonstrated on the PALMS dataset for end-to-end forest management workflows.
Forest imagery analysis often involves multiple tightly coupled vision tasks, which must be performed under substantial variation in geographic regions, sensors, and acquisition conditions. However, practitioners often lack a unified tool that is geospatial-native, cloud-optimized, and ML-integrated for end-to-end workflows spanning annotation, prediction, visualization, and downstream analysis at scale. We present AwakeForest, an interactive end-to-end platform designed for large-scale forest imagery that integrates model-assisted inference, automatic annotation, and human-in-the-loop refinement within a single workflow. Our platform supports plug-and-play integration of pretrained models and enables scalable interaction with forest imagery ranging from standard aerial scenes to large orthomosaics that can span several gigabytes to hundreds of gigabytes. AwakeForest produces analysis-ready outputs that can be directly used for downstream analysis and to support iterative model and annotation updates on new scenes. We demonstrate the system on the PALMS dataset and illustrate how AwakeForest supports an end-to-end workflow for practical forest management and analysis.