Automated Road and Building Extraction

Últimos artículos

• HSI-Road Relabeled: Surface-Aware Road-Scene Segmentation

  Imad Ali Shah, Imran Mehmood, Enda Ward, Martin Glavin, Edward Jones, Brian Deegan · 14 de septiembre de 2026

  The HSI-Road dataset provides paired RGB and 25-channel NIR (600--960~nm) images with binary masks but no surface-level labels.~This paper introduces a manually labeled six-class taxonomy: Background, Asphalt, Concrete, Dirt, Water, and Grass, and an RGB-to-NIR registration pipeline with correspondi…

• TSMini: A Simple Yet Highly Effective Trajectory Similarity Learning Model

  Yanchuan Chang, Dingyang Lyu, Xu Cai, Christian S. Jensen, Jianzhong Qi · 7 de septiembre de 2026

  Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approximate conventional trajectory similarity measures, exploiting their fast inference time once trained. Although efficient inference has been reported, challenges…

• Lightweight Machine Learning-Driven Monocular Sidewalk Path Extraction for Embedded Micromobility Navigation

  Lkhanaajav Mijiddorj, Yang Yan, Tyler Beringer, Bilguunzaya Mijiddorj, Alex N. Ho, Bin Xu, Binbin Weng · 27 de agosto de 2026

  Sidewalk-scale path extraction demands perception and planning that run reliably on compact, low-power hardware in cluttered, map-sparse environments. We present a monocular vision pipeline for sidewalk path extraction in micromobility systems that progresses through three design iterations, from a …

• Cross-View Urban Sensing: Mapping Subjective Streetscape Perception via AlphaEarth Embeddings and Urban Context

  Peilin Li, Pengfei Chen, Jingyu Wang, Zhifeng Yang, Tiansheng Chen, Mengjie Gong, Xiao Cheng · 18 de agosto de 2026

  Residents' perception of the urban streetscape is an important factor in public health, active mobility, and social wellbeing. Street view imagery (SVI) has emerged as a widely used data source for assessing these perceptual qualities, yet its uneven coverage and irregular updating limit large-scale…

• RoadVGGT: Road-Structure-Aware Feed-Forward Road Surface Reconstruction

  Han Jiao, Chen Liu, Jiakai Sun, Zhanjie Zhang, Mengyuan Yang, Yimeng Li, Mofan Zhou, Kun Zhan, Lei Zhao · 28 de julio de 2026

  Large-scale road surface reconstruction supports high-definition mapping, autonomous-driving perception, annotation, and simulation. Existing road-specialized optimization methods can produce high-quality road representations, but they typically require per-scene training and scene-dependent coverag…

• RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping

  Tianchen Deng, Zhiheng Feng, Wenhua Wu, Ziming Li, Siting Zhu, Hesheng Wang · 17 de julio de 2026

  Road surface mapping plays a crucial role in autonomous driving, supporting high-definition map generation, lane-level perception, and automatic road annotation. Recent mesh-based road surface reconstruction methods have shown promising results, but they still suffer from limited reconstruction qual…

• TerraDiT-$Ω$: Unified Spatial Control for Satellite Image Synthesis with Any Geospatial Primitive

  Brian Wei, Srikumar Sastry, Daniel Cher, Eric Xing, Nathan Jacobs · 1 de julio de 2026

  Generative models have achieved remarkable progress, yet applying them to satellite imagery remains challenging. Unlike natural imagery, satellite scenes are structured by spatially complex and semantically distinct geometries. Prior work addresses this complexity by adapting natural image framework…

• GeoFidelity-Bench: Evaluating Segment-Level Geographic Fidelity in Text-to-Image Street-View Generation

  Kaizhen Tan, Hanzhe Hong, Siru Tao · 23 de junio de 2026

  Text-to-image models can generate visually plausible city streets, but whether their outputs correspond to a requested road segment rather than a generic city prior remains unclear. We introduce GeoFidelity-Bench, a reference-panel benchmark for segment-conditioned geographic fidelity in street-view…

• Optimization-based Online Conformal Prediction for Multi-step Forecasting

  Ruipu Li, Daniel Menacho, Alexander Rodr\'iguez · 10 de junio de 2026

  Conformal prediction (CP) is well-suited for uncertainty quantification in time series forecasting due to its distribution-free coverage guarantees. However, existing multi-step methods often struggle to balance coverage validity with efficiency: they either calibrate horizons independently, ignorin…

• A Systematic Approach for Selecting Trajectories for Data Augmentation

  Adam Nordling · 10 de junio de 2026

  Trajectory data augmentation is a promising approach to mitigate data scarcity in machine learning applications, but its utility has been limited by the complexity of preserving spatio-temporal coherence. Although prior work demonstrated the viability of geometric perturbation, it relied on naive ra…

• OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs

  Dimitrios Michail, Eleni Saka, Ioannis Giannopoulos, Ioannis Papoutsis · 9 de junio de 2026

  We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) data. OSMGraphCLIP represents geographic environments as heterogeneous graphs of typed OSM features, preserving the topological and semantic relation…

• Building and Road Recognition in Dense Urban Informal Settlements: A Dataset and Benchmark

  Hongyu Long, Jiaxuan Liu, Rui Cao · 29 de mayo de 2026

  As a widespread form of informal settlements, urban villages present significant challenges for sustainable urban development and governance. Precise mapping of their infrastructure is essential, however, existing remote sensing datasets primarily focus on formal urban environments, lacking fine-gra…

• How to Relieve Distribution Shifts in Semantic Segmentation for Off-Road Environments

  Ji-Hoon Hwang, Daeyoung Kim, Hyung-Suk Yoon, Dong-Wook Kim, Seung-Woo Seo · 29 de mayo de 2026

  Semantic segmentation is crucial for autonomous navigation in off-road environments, enabling precise classification of surroundings to identify traversable regions. However, distinctive factors inherent to off-road conditions, such as source-target domain discrepancies and sensor corruption from ro…

• RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction

  Chenxu Peng, Chenxu Wang, Yimian Dai, Yongxiang Liu, Ming-Ming Cheng, Xiang Li · 27 de mayo de 2026

  Accurate road segmentation from aerial imagery is fundamental to many geospatial applications. However, existing datasets often suffer from limited scene diversity, low semantic granularity, and poor structural continuity, restricting their generalization across environments. To address these challe…

• Designing streetscapes from street-view imagery using diffusion models

  Yuzhou Chen, Yuebing Liang, Lingqian Hu, Kailai Sun, Qingqi Song, Chang Zhao, Shenhao Wang · 19 de mayo de 2026

  Street-view imagery (SVI) is widely used to quantify key indicators of urban environment, such as green- ery, sky, or road view indices. However, existing studies largely focus on measuring current streetscapes and rarely support the generation of alternative and non-existing urban scenarios, which …

• Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse

  Yunsong Fang (University of Macau), Tingyu Wang (Hangzhou Dianzi University), Zhedong Zheng (University of Macau) · 15 de mayo de 2026

  Drone-view geo-localization aims to match a query drone image, often captured under adverse weather conditions (e.g., rain, snow, fog), against a gallery of geo-tagged satellite images. Weather-induced degradations in the drone view, such as noise, reduced visibility, and partial occlusions, severel…

• NARA: Anchor-Conditioned Relation-Aware Contextualization of Heterogeneous Geoentities

  Jina Kim, Gengchen Mai, Lingyi Zhao, Khurram Shafique, Yao-Yi Chiang · 13 de mayo de 2026

  Geospatial foundation models have primarily focused on raster data such as satellite imagery, where self-supervised learning has been widely studied. Vector geospatial data instead represent the world as discrete geoentities with explicit geometry, semantics, and structured spatial relations, includ…

• TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations

  Maria Despoina Siampou, Gengchen Mai, Ni Lao, Jinmeng Rao, Neha Arora, Cyrus Shahabi, Shushman Choudhury · 11 de mayo de 2026

  Multimodal self-supervised learning (MSSL) has emerged as a key paradigm for pretraining geospatial foundation models. However, existing geospatial MSSL methods are mainly designed for static pairs of modalities, such as satellite imagery, street-view imagery, and text, where learning is driven by a…

• Laplacian Frequency Interaction Network for Rural Thematic Road Extraction

  Baiyan Chen, Weixin Zhai · 5 de mayo de 2026

  Rural thematic road network construction aims to extract topological road structures from movement trajectory images of agricultural machinery. However, this task faces challenges where downsampling methods commonly used in existing studies tend to blur the sparse high-frequency road structures, and…

• GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations

  Zeping Liu, Ni Lao, Zhangyu Wang, Junfeng Jiao, Gengchen Mai · 22 de abril de 2026

  Vision Transformer (ViT) has been widely used in computer vision tasks with excellent results by providing representations for a whole image or image patches. However, ViT lacks detailed localized image representations at arbitrary positions when applied to geospatial tasks that involve multiple geo…

• UNIGEOCLIP: Unified Geospatial Contrastive Learning

  Guillaume Astruc, Eduard Trulls, Jan Hosang, Loic Landrieu, Paul-Edouard Sarlin · 14 de abril de 2026

  The growing availability of co-located geospatial data spanning aerial imagery, street-level views, elevation models, text, and geographic coordinates offers a unique opportunity for multimodal representation learning. We introduce UNIGEOCLIP, a massively multimodal contrastive framework to jointly …

• PC-SAM: Patch-Constrained Fine-Grained Interactive Road Segmentation in High-Resolution Remote Sensing Images

  Chengcheng Lv, Rushi Li, Mincheng Wu, Xiufang Shi, Zhenyu Wen, Shibo He · 2 de abril de 2026

  Road masks obtained from remote sensing images effectively support a wide range of downstream tasks. In recent years, most studies have focused on improving the performance of fully automatic segmentation models for this task, achieving significant gains. However, current fully automatic methods are…

• DB SwinT: A Dual-Branch Swin Transformer Network for Road Extraction in Optical Remote Sensing Imagery

  Zongyang He, Xiangli Yang, Xian Gao, Zhiguo Wang · 26 de marzo de 2026

  With the continuous improvement in the spatial resolution of optical remote sensing imagery, accurate road extraction has become increasingly important for applications such as urban planning, traffic monitoring, and disaster management. However, road extraction in complex urban and rural environmen…

• UrbanVGGT: Scalable Sidewalk Width Estimation from Street View Images

  Kaizhen Tan, Fan Zhang · 25 de marzo de 2026

  Sidewalk width is an important indicator of pedestrian accessibility, comfort, and network quality, yet large-scale width data remain scarce in most cities. Existing approaches typically rely on costly field surveys, high-resolution overhead imagery, or simplified geometric assumptions that limit sc…

• A Large-Scale Remote Sensing Dataset and VLM-based Algorithm for Fine-Grained Road Hierarchy Classification

  Ting Han, Xiangyi Xie, Yiping Chen, Yumeng Du, Jin Ma, Aiguang Li, Jiaan Liu, Yin Gao · 24 de marzo de 2026

  In this work, we present SYSU-HiRoads, a large-scale hierarchical road dataset, and RoadReasoner, a vision-language-geometry framework for automatic multi-grade road mapping from remote sensing imagery. SYSU-HiRoads is built from GF-2 imagery covering 3631 km2 in Henan Province, China, and contains …