An official website of the United States government
Modern data analytics applications prefer to use column-storage formats due to their improved storage efficiency through encoding and compression. Parquet is the most popular file format for col- umn data storage that provides several of these benefits out of the box. However, geospatial data is not readily supported by Parquet. This paper introduces Spatial Parquet, a Parquet extension that efficiently supports geospatial data. Spatial Parquet inherits all the advantages of Parquet for non-spatial data, such as rich data types, compression, and column/row filtering. Additionally, it adds three new features to accommodate geospatial data. First, it introduces a geospatial data type that can encode all standard spatial geome- tries in a column format compatible with Parquet. Second, it adds a new lossless and efficient encoding method, termed FP-delta, that is customized to efficiently store geospatial coordinates stored in floating-point format. Third, it adds a light-weight spatial index that allows the reader to skip non-relevant parts of the file for increased read efficiency. Experiments on large-scale real data showed that Spatial Parquet can reduce the data size by a factor of three even without compression. Compression can further reduce the storage size. Additionally, Spatial Parquet can reduce the reading time by two orders of magnitude when the light-weight index is applied. This initial prototype can open new research directions to further improve geospatial data storage in column format.
more »
« less
Geospatial Computing from Data Lakes to Deep Learning Applications
This thesis explores geospatial vector data, including geometric shapes such as points, lines, and polygons. This data is crucial in navigation, urban planning, and many more applications. Geospatial computing is a multidisciplinary field that focuses on creating techniques and tools to handle large geospatial datasets. Given the reliance on data lakes to store large data sets in their raw formats, it is critical to have full support for geospatial datasets to enable scalable processing. To address this, we make two contributions in this area. First, we propose a column-oriented binary format called Spatial Parquet, which integrates geospatial vector data into Apache Parquet that enables significant data compression and efficient querying. Second, to improve support for semi-structured data, we introduce a distributed JSON processor for scalable SQL queries on large JSON datasets, including GeoJSON. It processes complex datasets like Open Street Map with features such as projection and filter push-down. Advances in Deep Learning (DL), including foundation models and Large Language Models (LLMs), offer opportunities for geospatial data analysis. We make three main contributions in this area. First, we study how to design DL models that can express a wide range of geospatial functions. We explore three representations: an image-based representa- tion using geo-referenced histograms (GeoImg), a graph-based point-set representation (Ge- oGraph), and a vector-based representation using a Fourier encoder (GeoVec). We formal- ize these representations and design corresponding models: ResNet and UNet for the first, PointNet++ for the second, and Poly2Vec with Transformers for the third. We evaluate all approaches on four spatial problems, showing the accuracy and effectiveness of the three approaches. Second, we create a benchmark called GS-QA for evaluating spatial question- answering with LLMs. A semi-automated process generates diverse question-answer pairs that cover various spatial objects, predicates, and complexities. An evaluation methodology is suggested with some experiments. Finally, a prototype for generating geospatial vector data from text prompts, called GeoGen I, is proposed. It has potential for applications such as spatial interpolation, data augmentation, and change analysis. We adapt diffusion models, traditionally used for generating realistic images, as geospatial data generators. We also explore their use for similarity search through geospatial data embeddings, highlighting the potential of vector databases in this domain. This thesis advances geospatial data processing, storage, analysis, and generation, opening new research pathways in geospatial computing.
more »
« less
- Award ID(s):
- 2046236
- PAR ID:
- 10611925
- Publisher / Repository:
- UC Riverside
- Date Published:
- Format(s):
- Medium: X
- Institution:
- UC Riverside
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
With recent advancements, large language models (LLMs) such as ChatGPT and Bard have shown the potential to disrupt many industries, from customer service to healthcare. Traditionally, humans interact with geospatial data through software (e.g., ArcGIS 10.3) and programming languages (e.g., Python). As a pioneer study, we explore the possibility of using an LLM as an interface to interact with geospatial datasets through natural language. To achieve this, we also propose a framework to (1) train an LLM to understand the datasets, (2) generate geospatial SQL queries based on a natural language question, (3) send the SQL query to the backend database, (4) parse the database response back to human language. As a proof of concept, a case study was conducted on real-world data to evaluate its performance on various queries. The results show that LLMs can be accurate in generating SQL code for most cases, including spatial joins, although there is still room for improvement. As all geospatial data can be stored in a spatial database, we hope that this framework can serve as a proxy to improve the efficiency of spatial data analyses and unlock the possibility of automated geospatial analytics.more » « less
-
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
-
Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a benchmark for generating structured and formatted spatial outputs from LLMs with a focus on enhancing spatial information generation. We present a multi-step workflow designed to improve the accuracy and efficiency of spatial data generation. The steps include generating spatial data (e.g., GeoJSON) and implementing a novel method for indexing R-tree structures. In addition, we explore and compare a series of methods commonly used by developers and researchers to enable LLMs to produce structured outputs, including fine-tuning, prompt engineering, and retrieval-augmented generation (RAG). We propose new metrics and datasets along with a new method for evaluating the quality and consistency of these outputs. Our findings offer valuable insights into the strengths and limitations of each approach, guiding practitioners in selecting the most suitable method for their specific use cases. This work advances the field of LLM-based structured spatial data output generation and supports the seamless integration of LLMs into real-world applications.more » « less
-
Generating realistic geospatial vector data is important for evaluat-ing algorithms, index structures, and systems under diverse condi-tions. Existing synthetic data generators typically rely on simplestatistical or procedural models that fail to capture the complexityof real-world spatial patterns. This paper introduces GeoGen I, agenerative framework that produces geospatial point distributionsfrom natural language prompts. The system combines contrastivelearning, region context, and a diffusion-based generator to createplausible datasets. In the experiments, we test variations of themodel and provide both qualitative and quantitative evaluations.Our experiments show that it can generate spatial patterns alignedwith different prompts. While the results are promising, many chal-lenges still remain, including in dataset curation and quality, andthe model’s ability to capture subtle geospatial constraints.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Dissertation:
- The DOI is not currently available.
