AAG 2025 Symposium on Spatial AI & Data Science for Sustainability: Deep Learning Approaches for Multi-Source Data Processing and Analysis
Date: 3/27/2025
Time: 12:50 PM - 2:10 PM
Room: 356, Level 3, Huntington Place
Type: Paper
Recorded:
Theme: Making Spaces of Possibility
Curated Track:
Sponsor Group(s):
Cyberinfrastructure Specialty Group, Geographic Information Science and Systems Specialty Group, Spatial Analysis and Modeling Specialty Group
Organizer(s):
Wen Zhou University of Illinois - Department of Geography and GIS
Wei Hu University of Illinois - Department of Geography and GIS
Chair(s):
Wen Zhou, University of Illinois - Department of Geography and GIS
,
Description:
In today’s data-rich era, researchers and practitioners encounter both unprecedented opportunities and challenges in deciphering complex environments through multi-source data analysis. The influx of data from remote sensing images, point clouds, social media imagery, and geospatial textual information offers a wealth of knowledge crucial for urban studies, environmental monitoring, and human-centered applications. The heterogeneity of these data sources also brings about unique complexities in terms of integration and processing.
Recent advancements in computer vision (CV), large language models (LLMs), and multimodal deep learning have enabled novel methods to bridge diverse data modalities and extract valuable insights from images, text, and sensor outputs. Techniques such as multimodal fusion, semantic segmentation, object detection, time series analysis, and natural language processing are breaking new ground in transforming multi-source data into actionable intelligence.
This session aims to advance the application of deep learning techniques for multi-source data processing. We seek to explore methods that address the distinctive characteristics of various data types and offer practical solutions for their analysis. We invite researchers to share their work, showcasing innovative approaches to multi-source data integration and analysis. Additionally, we aim to engage potential new users interested in these methodologies, fostering deeper multidisciplinary interactions on this topic.
Presentations (if applicable) and Session Agenda:
Faezeh Najafzadeh, University of Oklahoma |
Assessing the Drought Effect on Crop Productivity for Precision Agriculture using Multimodal Remote Sensing Data and Deep Learning Models |
Ikramul Hasan, The Ohio State University |
Quantifying Surface Mining Expansion and Reclamation Using Deep Learning-based ConvoLSTM Model and Satellite Images: A Case Study in Lapland Region of Finland |
Wen Zhou, University of Illinois - Department of Geography and GIS |
Urban livability evaluation based upon multimodal deep learning |
H Rainak Khan Real, Ohio State University |
Fine-scale canopy height mapping of boreal forest by integrating ICESat-2, PlanetScope & ArcticDEM |
Subhasis Ghosh, Auburn University |
Introducing NDUI+: A fused DMSP-VIIRS based multidecadal, high-resolution global normalized difference urban index (NDUI) dataset |
Non-Presenting Participants
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AAG 2025 Symposium on Spatial AI & Data Science for Sustainability: Deep Learning Approaches for Multi-Source Data Processing and Analysis
Description
Type: Paper
Date: 3/27/2025
Time: 12:50 PM - 2:10 PM
Room: 356, Level 3, Huntington Place
Contact the Primary Organizer
Wen Zhou University of Illinois - Department of Geography and GIS
wz53@illinois.edu
Session sponsored by: