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Found 12 papers, 0 papers with code
OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery
While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters.
Pretraining Billion-scale Geospatial Foundational Models on Frontier
Although large FMs have demonstrated significant impact in natural language processing and computer vision, efforts toward FMs for geospatial applications have been restricted to smaller size models, as pretraining larger models requires very large computing resources equipped with state-of-the-art hardware accelerators.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
GeoAI at ACM SIGSPATIAL: The New Frontier of Geospatial Artificial Intelligence Research
Geospatial Artificial Intelligence (GeoAI) is an interdisciplinary field enjoying tremendous adoption.
Proceedings of KDD 2020 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning
Despite these growing perils, there remains a notable paucity of data science research to scientifically inform equitable public policy decisions for improving the livelihood of at-risk populations.
Proceedings of KDD 2021 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning
Despite these growing perils, there remains a notable paucity of data science research to scientifically inform equitable public policy decisions for improving the livelihood of at-risk populations.
Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation
These pre-trained DL models' ability to generalize in clinical settings is poor because of the changes in data distributions between publicly available and privately held radiographs.
Learning to Count Grave Sites for Cemetery Observation Models With Satellite Imagery
Understanding how people occupy open spaces is important for research in support of population modeling, policy, national security, emergency response, and sustainability.
Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics
By taking advantage of Apache Spark, Nvidia DGX1, and DGX2 computing platforms, we demonstrate unprecedented compute speed-ups for deep learning inference on pixel labeling workloads; processing 21, 028~Terrabytes of imagery data and delivering an output maps at area rate of 5. 245sq. km/sec, amounting to 453, 168 sq. km/day - reducing a 28 day workload to 21~hours.
Building Extraction at Scale using Convolutional Neural Network: Mapping of the United States
The quality of extracted buildings and processing time demonstrated the proposed CNN-based framework fits the need of building extraction at scale.
Exploiting Convolutional Representations for Multiscale Human Settlement Detection
We test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework.
Hashed Binary Search Sampling for Convolutional Network Training with Large Overhead Image Patches
Very large overhead imagery associated with ground truth maps has the potential to generate billions of training image patches for machine learning algorithms.
