112 citations · 128 across the 20 of their papers we have counts for
20 papers
Risk-Aware LLM Agents for Geospatial Data Retrieval: Design and Preliminary Adversarial Evaluation
Kyle Gao, Joel Cumming, Jonathan Li +2
We present an LLM-driven framework for retrieving remote sensing data from cloud-based geospatial catalogues using natural language queries. The system converts user intent into st…
Securing Multi-Agent GIS Systems: Risk Evaluation and Prompt Hardening Optimization
Kyle Gao, Pranavi Kotta, Linlin Xu +2
Agentic systems are increasingly integrated with geographic information systems (GIS), where multi-agent coordination enables complex conversational and spatial analysis but introd…
Real-Scale Island Area and Coastline Estimation using Only its Place Name or Coordinates
Quanyun Wu, Kyle Gao, Wentao Sun +4
Accurate measurement of island area and coastline length is crucial for coastal zone monitoring and oceanographic analysis. However, traditional measurement and mapping methods usu…
Rapid Forest Fuel Load Estimation via Virtual Remote Sensing and Metric-Scale Feed-Forward 3D Reconstruction
Quanyun Wu, Kyle Gao, Wentao Sun +6
Accurate quantification of forest coverage and combustible biomass (fuel load) is critical for wildfire risk assessment and ecosystem management. However, traditional methods relyi…
KitchenTwin: Semantically and Geometrically Grounded 3D Kitchen Digital Twins
Quanyun Wu, Kyle Gao, Daniel Long +3
Embodied AI training and evaluation require object-centric digital twin environments with accurate metric geometry and semantic grounding. Recent transformer-based feedforward reco…
Trustworthy Data-Driven Wildfire Risk Prediction and Understanding in Western Canada
Zhengsen Xu, Lanying Wang, Sibo Cheng +12
In recent decades, the intensification of wildfire activity in western Canada has resulted in substantial socio-economic and environmental losses. Accurate wildfire risk prediction…
Sparse Data Tree Canopy Segmentation: Fine-Tuning Leading Pretrained Models on Only 150 Images
David Szczecina, Hudson Sun, Anthony Bertnyk +3
Tree canopy detection from aerial imagery is an important task for environmental monitoring, urban planning, and ecosystem analysis. Simulating real-life data annotation scarcity,…
Instructor-Worker Large Language Model System for Policy Recommendation: a Case Study on Air Quality Analysis of the January 2025 Los Angeles Wildfires
Kyle Gao, Dening Lu, Liangzhi Li +4
The Los Angeles wildfires of January 2025 caused more than 250 billion dollars in damage and lasted for nearly an entire month before containment. Following our previous work, the…
Digital Twin Buildings: 3D Modeling, GIS Integration, and Visual Descriptions Using Gaussian Splatting, ChatGPT/Deepseek, and Google Maps Platform
Kyle Gao, Dening Lu, Liangzhi Li +4
Urban digital twins are virtual replicas of cities that use multi-source data and data analytics to optimize urban planning, infrastructure management, and decision-making. Towards…
Gaussian Building Mesh (GBM): Extract a Building's 3D Mesh with Google Earth and Gaussian Splatting
Kyle Gao, Liangzhi Li, Hongjie He +3
Recently released open-source pre-trained foundational image segmentation and object detection models (SAM2+GroundingDINO) allow for geometrically consistent segmentation of object…
Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery
Willow Liu, Shuxin Qiao, Kyle Gao +4
This research addresses the need for high-definition (HD) maps for autonomous vehicles (AVs), focusing on road lane information derived from aerial imagery. While Earth observation…
UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation
Pengfei Zhang, Chang Li, Yongjun Zhang +3
Remotely sensed imagery interpretation (RSII) faces the three major problems: (1) objective representation of spatial distribution patterns; (2) edge uncertainty problem caused by…
Enhanced 3D Urban Scene Reconstruction and Point Cloud Densification using Gaussian Splatting and Google Earth Imagery
Kyle Gao, Dening Lu, Hongjie He +2
3D urban scene reconstruction and modelling is a crucial research area in remote sensing with numerous applications in academia, commerce, industry, and administration. Recent adva…
Efficient Point Transformer with Dynamic Token Aggregating for LiDAR Point Cloud Processing
Dening Lu, Jun Zhou, Kyle +3
Recently, LiDAR point cloud processing and analysis have made great progress due to the development of 3D Transformers. However, existing 3D Transformer methods usually are computa…
3D Learnable Supertoken Transformer for LiDAR Point Cloud Scene Segmentation
Dening Lu, Jun Zhou, Kyle Gao +2
3D Transformers have achieved great success in point cloud understanding and representation. However, there is still considerable scope for further development in effective and eff…
Dynamic Clustering Transformer Network for Point Cloud Segmentation
Dening Lu, Jun Zhou, Kyle Yilin Gao +4
Point cloud segmentation is one of the most important tasks in computer vision with widespread scientific, industrial, and commercial applications. The research thereof has resulte…
NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)
Kyle Gao, Yina Gao, Hongjie He +3
In March 2020, Neural Radiance Field (NeRF) revolutionized Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis. NeRF models h…
3DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation
Dening Lu, Kyle Gao, Qian Xie +2
Although the application of Transformers in 3D point cloud processing has achieved significant progress and success, it is still challenging for existing 3D Transformer methods to…
3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification
Dening Lu, Qian Xie, Linlin Xu +1
Although accurate and fast point cloud classification is a fundamental task in 3D applications, it is difficult to achieve this purpose due to the irregularity and disorder of poin…
The impact of data volume on performance of deep learning based building rooftop extraction using very high spatial resolution aerial images
Hongjie He, Ke Yang, Yuwei Cai +15
Building rooftop data are of importance in several urban applications and in natural disaster management. In contrast to traditional surveying and mapping, by using high spatial re…