GeoEngine: A Platform for Production-Ready Geospatial Research
Nov 09, 2023
Geospatial machine learning has seen tremendous academic advancement, but its practical application has been constrained by difficulties with operationalizing performant and reliable solutions. Sourcing satellite imagery in real-world settings, handling terabytes of training data, and managing machine learning artifacts are a few of the challenges that have severely limited downstream innovation. In this paper we introduce the GeoEngine platform for reproducible and production-ready geospatial machine learning research. GeoEngine removes key technical hurdles to adopting computer vision and deep learning-based geospatial solutions at scale. It is the first end-to-end geospatial machine learning platform, simplifying access to insights locked behind petabytes of imagery. Backed by a rigorous research methodology, this geospatial framework empowers researchers with powerful abstractions for image sourcing, dataset development, model development, large scale training, and model deployment. In this paper we provide the GeoEngine architecture explaining our design rationale in detail. We provide several real-world use cases of image sourcing, dataset development, and model building that have helped different organisations build and deploy geospatial solutions.
CVPR Demo 2022
Sagar Verma , Siddharth Gupta , Hal Shin , Akash Panigrahi , Shubham Goswami , Shweta Pardeshi , Natanael Exe , Ujwal Dutta , Tanka Raj Joshi , Nitin Bhojwani
Post Wildfire Burnt-up Detection using Siamese UNet
In this article, we present an approach for detecting burnt area due to wild fire in Sentinel-2 images by leveraging the power of Siamese neural networks. By employing a Siamese network, we are able to efficiently encode the feature extraction process for pairs of images. This is achieved by utilizing two branches within the Siamese network, which capture and combine information at different resolutions to make predictions. The weights are shared between these two branches in siamese networks. This design allows to effectively analyze the changes between two remote sensing images, enabling precise identification of areas impacted by forest wildfires in the state of California as part of ChaBuD challenge thereby assisting local authorities in effectively monitoring the impacted regions and facilitating the restoration process. We experimented with various model architectures to train ChaBuD dataset and carefully evaluated the performance. Through rigorous testing and analysis, we have achieved promising results, ultimately obtaining a final private score (IoU) of 0.7495 on the hidden test dataset. The code is available at https://github.com/kavyagupta/chabud. We also deploy the final model as a point solution for anyone to use at https://firemap.io.
Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
The advent of multitemporal high resolution data, like the Copernicus Sentinel-2, has enhanced significantly the potential of monitoring the earth's surface and environmental dynamics. In this paper, we present a novel deep learning framework for urban change detection which combines state-of-the-art fully convolutional networks (similar to U-Net) for feature representation and powerful recurrent networks (such as LSTMs) for temporal modeling. We report our results on the recently publicly available bi-temporal Onera Satellite Change Detection (OSCD) Sentinel-2 dataset, enhancing the temporal information with additional images of the same region on different dates. Moreover, we evaluate the performance of the recurrent networks as well as the use of the additional dates on the unseen test-set using an ensemble cross-validation strategy. All the developed models during the validation phase have scored an overall accuracy of more than 95%, while the use of LSTMs and further temporal information, boost the F1 rate of the change class by an additional 1.5%.