AwakeForest: An Interactive Geospatial Platform for Large-Scale Forest Imagery

2026-06-22

AI Summary

Existing forest image analysis lacks a unified end-to-end framework capable of efficiently handling large-scale data across multiple tasks, regions, and sensors. This work proposes the first natively geospatial, cloud-optimized, and deeply machine learning–integrated interactive platform that combines pretrained vision models, automated annotation, and human-in-the-loop refinement mechanisms. It enables efficient processing within a single workflow—from standard aerial imagery to orthomosaic datasets exceeding 100 GB—while directly producing downstream-ready outputs. Validated on the PALMS dataset, the platform supports continuous iterative updates of models and annotations for novel scenarios, facilitating scalable and adaptive forest monitoring.

Abstract

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.

Problem

• forest imagery analysis
• geospatial platform
• large-scale vision tasks
• end-to-end workflow
• cloud-optimized

Innovation

• geospatial platform
• model-assisted annotation
• human-in-the-loop
• large-scale forest imagery
• cloud-optimized