Advances in scalable and intelligent geospatial analytics : challenges and applications /
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Edition: | First edition. |
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Imprint: | Boca Raton, FL : CRC Press, 2023. ©2023 |
Description: | 1 online resource ( xvi, 405 pages) : illustrations (some color), maps (chiefly color) |
Language: | English |
Subject: | |
Format: | E-Resource Book |
URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/13385865 |
Table of Contents:
- Geospatial Technology
- Developments, Present Scenario and Research Challenges
- Perspectives on Geospatial Artificial Intelligence Platforms for Multimodal Spatiotemporal Datasets
- Temporal Dynamics of Place and Mobility
- Geospatial Knowledge Graph Construction Workflow for Semantics-enabled Remote Sensing Scene Understanding
- Geosemantic Standards-driven Intelligent Information Retrieval Framework for 3D LiDAR Point Clouds
- Geospatial Analytics Using Natural Language Processing
- A Scalable Automated Satellite Data Downloading and Processing Pipeline Developed on AWS Cloud for Agricultural Applications
- Providing Geospatial Intelligence through a Scalable Imagery Pipeline
- Distributed Deep Learning and its Application in Geo-spatial Analytics
- High Performance Computing for Processing Big Geospatial Disaster Data
- Dashboard for Earth Observation
- Visual Exploration of LiDAR Point Clouds
- Towards a Smart Metaverse City: Immersive Realism and 3D Visualization of Digital Twin Cities
- Current UAS Capabilities for Geospatial Spectral Solutions
- Flood Mapping and Damage Assessment using Sentinel
- 1 & 2 in Google Earth Engine of Port Berge & Mampikony Districts, Sophia Region, Madagascar
- Fuzzy-based Meta-heuristic and Bi-variate Geo-statistical Modelling for Spatial Prediction of landslides
- Understanding the Dynamics of the City through Crowdsourced Datasets: A Case Study of Indore City
- A Hybrid Model for the Prediction of Land use/ Land cover Pattern in Kurunegala City, Sri Lanka
- Spatio-temporal Dynamics of Tropical Deciduous Forests Under Climate Change Scenarios in India
- A Survey of Machine Learning Techniques in Forestry Applications Using SAR Data.