More Like this

1. geodl: An R package for geospatial deep learning semantic segmentation using torch and terra

   https://doi.org/10.1371/journal.pone.0315127  

   Maxwell, Aaron E; Farhadpour, Sarah; Das, Srinjoy; Yang, Yalin (December 2024, PLOS ONE)

   Sun, Xiaoyong (Ed.)

   Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer’s accuracies, class-level precisions or user’s accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development. 

   more » « less
2. HierGP: Hierarchical Grid Partitioning for Scalable Geospatial Data Analytics

   https://doi.org/10.1145/3699511  

   Oje, Olufunso; Stirewalt, Tashi; Amram, Ofer; Hystad, Perry; Amiri, Solmaz; Gebremedhin, Assefaw (October 2024, ACM Transactions on Spatial Algorithms and Systems)

   Application domains such as environmental health science, climate science, and geosciences—where the relationship between humans and the environment is studied—are constantly evolving and require innovative approaches in geospatial data analysis. Recent technological advancements have led to the proliferation of high-granularity geospatial data, enabling such domains but posing major challenges in managing vast datasets that have high spatiotemporal similarities. We introduce the Hierarchical Grid Partitioning (HierGP) framework to address this issue. Unlike conventional discrete global grid systems, HierGP dynamically adapts to the data’s inherent characteristics. At the core of our framework is the Map Point Reduction (MPR) algorithm, designed to aggregate and then collapse data points based on user-defined similarity criteria. This effectively reduces data volume while preserving essential information. The reduction process is particularly effective in handling environmental data from extensive geographical regions. We structure the data into a multilevel hierarchy from which a reduced representative dataset can be extracted. We compare the performance of HierGP against several state-of-the-art geospatial indexing algorithms and demonstrate that HierGP outperforms the existing approaches in terms of runtime, memory footprint, and scalability. We illustrate the benefits of the HierGP approach using two representative applications: analysis of over 289 million location samples from a registry of participants and efficient extraction of environmental data from large polygons. While the application demonstration in this work has focused on environmental health, the methodology of the HierGP framework can be extended to explore diverse geospatial analytics domains. 

   more » « less
3. Efficient Parallel and Adaptive Partitioning for Load-balancing in Spatial Join

   Yang, Jie; Puri, Satish (May 2020, 34th IEEE International Parallel & Distributed Processing Symposium)

   Due to the developments of topographic techniques, clear satellite imagery, and various means for collecting information, geospatial datasets are growing in volume, complexity, and heterogeneity. For efficient execution of spatial computations and analytics on large spatial data sets, parallel processing is required. To exploit fine-grained parallel processing in large scale compute clusters, partitioning in a load-balanced way is necessary for skewed datasets. In this work, we focus on spatial join operation where the inputs are two layers of geospatial data. Our partitioning method for spatial join uses Adaptive Partitioning (ADP) technique, which is based on Quadtree partitioning. Unlike existing partitioning techniques, ADP partitions the spatial join workload instead of partitioning the individual datasets separately to provide better load-balancing. Based on our experimental evaluation, ADP partitions spatial data in a more balanced way than Quadtree partitioning and Uniform grid partitioning. ADP uses an output-sensitive duplication avoidance technique which minimizes duplication of geometries that are not part of spatial join output. In a distributed memory environment, this technique can reduce data communication and storage requirements compared to traditional methods. To improve the performance of ADP, an MPI+Threads based parallelization is presented. With ParADP, a pair of real world datasets, one with 717 million polylines and another with 10 million polygons, is partitioned into 65,536 grid cells within 7 seconds. ParADP performs well with both good weak scaling up to 4,032 CPU cores and good strong scaling up to 4,032 CPU cores. 

   more » « less
4. Advanced Cyberinfrastructure to Enable Search of Big Climate Datasets in THREDDS

   https://doi.org/10.3390/ijgi8110494  

   Juozas Gaigalas, Liping Di (November 2019, ISPRS international journal of geoinformation)

   Understanding the past, present, and changing behavior of the climate requires close collaboration of a large number of researchers from many scientific domains. At present, the necessary interdisciplinary collaboration is greatly limited by the difficulties in discovering, sharing, and integrating climatic data due to the tremendously increasing data size. This paper discusses the methods and techniques for solving the inter-related problems encountered when transmitting, processing, and serving metadata for heterogeneous Earth System Observation and Modeling (ESOM) data. A cyberinfrastructure-based solution is proposed to enable effective cataloging and two-step search on big climatic datasets by leveraging state-of-the-art web service technologies and crawling the existing data centers. To validate its feasibility, the big dataset served by UCAR THREDDS Data Server (TDS), which provides Petabyte-level ESOM data and updates hundreds of terabytes of data every day, is used as the case study dataset. A complete workflow is designed to analyze the metadata structure in TDS and create an index for data parameters. A simplified registration model which defines constant information, delimits secondary information, and exploits spatial and temporal coherence in metadata is constructed. The model derives a sampling strategy for a high-performance concurrent web crawler bot which is used to mirror the essential metadata of the big data archive without overwhelming network and computing resources. The metadata model, crawler, and standard-compliant catalog service form an incremental search cyberinfrastructure, allowing scientists to search the big climatic datasets in near real-time. The proposed approach has been tested on UCAR TDS and the results prove that it achieves its design goal by at least boosting the crawling speed by 10 times and reducing the redundant metadata from 1.85 gigabytes to 2.2 megabytes, which is a significant breakthrough for making the current most non-searchable climate data servers searchable. 

   more » « less
5. Water in Geospatial Higher Education: A Discussion of Lessons Learned

   Ignatius, A.R.; Coleman, L.; Kurimo-Beechuk, E.; McDonald, J.; Milligan, R.; Mishra, D.R.; Page, M. (January 2021, Georgia Water Resources Conference)

   null (Ed.)

   Geospatial technologies and geographic methods are foundational skills in modern water resources monitoring, research, management, and policy-making. Understanding and sustaining healthy water resources depends on spatial awareness of watersheds, land use, hydrologic networks, and the communities that depend on these resources. Water professionals across disciplines are expected to have familiarity with hydrologic geospatial data. Proficiency in spatial thinking and competency reading hydrologic maps are essential skills. In addition, climate change and non-stationary ecological conditions require water specialists to utilize dynamic, time-enabled spatiotemporal datasets to examine shifting patterns and changing environments. Future water specialists will likely require even more advanced geospatial knowledge with the implementation of distributed internet-of-things sensor networks and the collection of mobility data. To support the success of future water professionals and increase hydrologic awareness in our broader communities, teachers in higher education must consider how their curriculum provides students with these vital geospatial skills. This paper considers pedagogical perspectives from educators with expertise in remote sensing, geomorphology, human geography, environmental science, ecology, and private industry. These individuals share a wealth of experience teaching geographic techniques such as GIS, remote sensing, and field methods to explore water resources. The reflections of these educators provide a snapshot of current approaches to teaching water and geospatial techniques. This commentary captures faculty experiences, ambitions, and suggestions for teaching at this moment in time. 

   more » « less