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Found 9 papers, 4 papers with code
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships.
Fooling LiDAR Perception via Adversarial Trajectory Perturbation
LiDAR point clouds collected from a moving vehicle are functions of its trajectories, because the sensor motion needs to be compensated to avoid distortions.
RSGPT: A Remote Sensing Vision Language Model and Benchmark
This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of RS.
Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud Classification
In this paper, we proposed a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling.
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
Based on this convolution module, we further developed a multi-scale fully convolutional neural network with downsampling and upsampling blocks to enable hierarchical point feature learning.
Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network
To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process raw point clouds based on PointNet-like architectures.
AETree: Areal Spatial Data Generation
Areal spatial data represent not only geographical locations but also sizes and shapes of physical objects such as buildings in a city.
AutoEncoding Tree for City Generation and Applications
Based on this dataset, we propose AETree, a tree-structured auto-encoder neural network, for city generation.
How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?
In the field of robotics and automation, navigation systems based on Large Language Models (LLMs) have recently shown impressive performance.
