Abstract: Satellite observations provide invaluable data across different spatio-temporal scales. These data enable us to build models for applications such as land cover classification, agricultural monitoring, surface water mapping, biodiversity monitoring, among others. Meanwhile, machine learning (ML) techniques can be utilized to advance these applications, and develop faster, more efficient and scalable models. These techniques learn from training datasets that are generated from image annotation or ground reference observations. However, to develop accurate ML-based models, and be able to validate their accuracy, we need to use benchmark training datasets that are representative of the diversity of the target variable, and openly accessible to all researchers and developers.
To address this requirement, Radiant Earth Foundation has established Radiant MLHub to foster sharing of geospatial training data for different thematic applications. Radiant MLHub is hosted on the cloud and users will be able to search for different training datasets, and quickly ingest them into their pipelines using an API. To increase interoperability of training datasets generated by different institutions, Radiant MLHub has adopted the SpatioTemporal Asset Catalog (STAC) as the standard for data cataloging.
In this presentation, I will review the architecture of Radiant MLHub, its API access and the STAC definition for training data. Next, I will present two applications on using ML models for LC classification from multi-spectral data and surface water detection from Synthetic Aperture (SAR) data.
Speaker bio: Hamed Alemohammad is the Chief Data Scientist at Radiant Earth Foundation, leading the development of Radiant MLHub as an opensource cloud native commons for Machine Learning applications using Earth Observations. He has extensive expertise in machine learning, remote sensing and imagery techniques particularly in developing new algorithms for multi-spectral satellite and airborne based observations. He also serves as an elected member of the American Geophysical Union’s technical committee on remote sensing. Prior to joining Radiant Earth, he was a Research Scientist at Columbia University. Hamed received his PhD in Civil and Environmental Engineering from MIT in 2014.
Abstract: Landscape classification is the task of using imagery to map defined features on the landscape. As computer technology and data science methodology advances, new techniques for this problem emerge. Modern machine learning (ML) utilizing neural networks (NN) – is becoming an industry-standard data science approach for a variety of applications. In particular, procedures of analysis for the task of computer vision (CV) are particularly adept and well-understood) at the task of computer vision (CV).
However, current landscape classification necessarily exposes trade-offs between accuracy, spatial granularity, and resources required. CV offers a unique combination of speed and accuracy, while still producing feature mappings rather than simple pixel classifications. Compared to other explicit feature extractors, such as object-based image analysis (OBIA), CV can be a cost-effective a powerful methodology for obtaining features from difficult-to-classify image domains.
This application shows how an off-the-shelf Deep Neural Network (DNN) algorithm – Inception v2 – was retrained into a production classifier and applied to the problem of locating and sizing cannabis production on private lands in Trinity County. This application demonstrates the strengths and limitations for applying this method at the landscape scale.
The presentation concludes with ‘next steps’ and identifies developing technologies and architectures that mitigate some of the limitations in the current application.
Speaker bio: Daryl Van Dyke serves as the spatial analyst for the USFWS, in Science Applications and Strategic Habitat Conservation. I have a interdisciplinary background, with a focus on community and environment as well as second BS and MS in Environmental Engineering. My thesis focused on using two-dimensional hydrodynamics for fish passage culvert retrofit design. As a federal servant, and a programmer, I've looked at the developing technologies of LiDAR, Structure-from-Motion, and ML as pivotal to the task of landscape analysis and conservation design. Non-technical interests in federal service include integrating analytic workflows, encouraging cross-program collaboration, and building accountability and reproducible science in resource management.