Search Results for author:
Found 17 papers, 9 papers with code
MapColorAI: Designing Contextually Relevant Choropleth Map Color Schemes Using a Large Language Model
On the other hand, it is difficult to fulfill some ambiguous and personalized coloring needs of users, such as requests for 'summer-like' map colors.
TS-SatMVSNet: Slope Aware Height Estimation for Large-Scale Earth Terrain Multi-view Stereo
3D terrain reconstruction with remote sensing imagery achieves cost-effective and large-scale earth observation and is crucial for safeguarding natural disasters, monitoring ecological changes, and preserving the environment. Recently, learning-based multi-view stereo~(MVS) methods have shown promise in this task.
UniRS: Unifying Multi-temporal Remote Sensing Tasks through Vision Language Models
The domain gap between remote sensing imagery and natural images has recently received widespread attention and Vision-Language Models (VLMs) have demonstrated excellent generalization performance in remote sensing multimodal tasks.
Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs
In this paper, we describe a novel unsupervised learning scheme for accelerating the solution of a family of mixed integer programming (MIP) problems.
Using a negative spatial auto-correlation index to evaluate and improve intrinsic TagMap's multi-scale visualization capabilities
The popularity of tag clouds has sparked significant interest in the geographic research community, leading to the development of map-based adaptations known as intrinsic tag maps.
Cloud-Edge-Terminal Collaborative AIGC for Autonomous Driving
In dynamic autonomous driving environment, Artificial Intelligence-Generated Content (AIGC) technology can supplement vehicle perception and decision making by leveraging models' generative and predictive capabilities, and has the potential to enhance motion planning, trajectory prediction and traffic simulation.
Optimized 3D Point Labeling with Leaders Using the Beams Displacement Method
Then we use the Beams Displacement Method to iteratively calculate new positions for the labels.
RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent
The unprecedented advancements in Multimodal Large Language Models (MLLMs) have demonstrated strong potential in interacting with humans through both language and visual inputs to perform downstream tasks such as visual question answering and scene understanding.
Generating grid maps via the snake model
However, existing approaches typically displace region centroids and boundary nodes separately, potentially resulting in self-intersected boundaries and compromised relative orientation relations between regions.
UAV-VisLoc: A Large-scale Dataset for UAV Visual Localization
Our dataset includes 6, 742 drone images and 11 satellite maps, with metadata such as latitude, longitude, altitude, and capture date.
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification
Besides, pioneer ZSL models use convolutional neural networks pre-trained on ImageNet, which focus on the main objects appearing in each image, neglecting the background context that also matters in RS scene classification.
Jointly Optimized Global-Local Visual Localization of UAVs
Our GLVL network is a two-stage visual localization approach, combining a large-scale retrieval module that finds similar regions with the UAV flight scene, and a fine-grained matching module that localizes the precise UAV coordinate, enabling real-time and precise localization.
ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth interval
Moreover, our method also achieves the lowest $e_{1}$ and $e_{3}$ on the BlendedMVS dataset and the highest Acc and $F_{1}$-score on the ETH 3D dataset, surpassing all listed methods. Project website: https://github. com/zs670980918/ARAI-MVSNet
Inferring High-level Geographical Concepts via Knowledge Graph and Multi-scale Data Integration: A Case Study of C-shaped Building Pattern Recognition
To improve efficiency and effectiveness, we integrate multi-scale data using a knowledge graph, focusing on the recognition of C-shaped building patterns.
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
Combining Supervised and Un-supervised Learning for Automatic Citrus Segmentation
Compared with most of the existing citrus segmentation methods, our method uses a small amount of supervised data and a large number of unsupervised data, while learning the pixel level location information and the temporal information of citrus changes to enhance the segmentation effect.
AMRNet: Chips Augmentation in Aerial Images Object Detection
Specifically, we propose a scale adaptive module, which dynamically adjusts chip size to balance object scale, narrowing scale variation in training.
