DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Institute of Technology（北京理工大学）；National and Kapodistrian University of Athens（雅典国立卡波季斯特里安大学）；China Aero Geophysical Survey and Remote Sensing Center for Natural Resources（中国自然资源航空物探遥感中心）；La Trobe University（乐卓博大学）
Ying Wang, Yuexing Peng, Xinran Liu, Wei Li, George C. Alexandropoulos, Junchuan Yu, Daqing Ge, Wei Xiang
Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide variety of applications, such as autonomous driving, path planning, and road navigation. Due to the long and thin shape as well as the shades induced by vegetation and buildings, small-sized roads are more difficult to discern. In order to improve the reliability and accuracy of small-sized road extraction when roads of multiple sizes coexist in an HRSI, an enhanced deep neural network model termed Dual-Decoder-U-Net (DDU-Net) is proposed in this paper. Motivated by the U-Net model, a small decoder is added to form a dual-decoder structure for more detailed features. In addition, we introduce the dilated convolution attention module (DCAM) between the encoder and decoders to increase the receptive field as well as to distill multi-scale features through cascading dilated convolution and global average pooling. The convolutional block attention module (CBAM) is also embedded in the parallel dilated convolution and pooling branches to capture more attention-aware features. Extensive experiments are conducted on the Massachusetts Roads dataset with experimental results showing that the proposed model outperforms the state-of-the-art DenseUNet, DeepLabv3+ and D-LinkNet by 6.5%, 3.3%, and 2.1% in the mean Intersection over Union (mIoU), and by 4%, 4.8%, and 3.1% in the F1 score, respectively. Both ablation and heatmap analyses are presented to validate the effectiveness of the proposed model.
GLD-Road:A global-local decoding road network extraction model for remote sensing images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；Heilongjiang Geographic Information Engineering Institute（黑龙江地理信息工程研究所）
Road surface extraction from remote sensing images using deep learning methods has achieved good performance, while most of the existing methods are based on fully supervised learning, which requires a large amount of training data with laborious per-pixel annotation. In this paper, we propose a scribble-based weakly supervised road surface extraction method named ScRoadExtractor, which learns from easily accessible scribbles such as centerlines instead of densely annotated road surface ground-truths. To propagate semantic information from sparse scribbles to unlabeled pixels, we introduce a r…
We propose an automated method to estimate a road segment's free-flow speed from overhead imagery and road metadata. The free-flow speed of a road segment is the average observed vehicle speed in ideal conditions, without congestion or adverse weather. Standard practice for estimating free-flow speeds depends on several road attributes, including grade, curve, and width of the right of way. Unfortunately, many of these fine-grained labels are not always readily available and are costly to manually annotate. To compensate, our model uses a small, easy to obtain subset of road features along wit…
DB SwinT: A Dual-Branch Swin Transformer Network for Road Extraction in Optical Remote Sensing Imagery
School of Information Science and Engineering（信息科学与工程学院）；Chongqing Jiaotong University（重庆交通大学）；College of Information Engineering（信息工程学院）；Inner Mongolia University of Technology（内蒙古科技大学）；Inner Mongolia Key Laboratory of Radar Technology and Application（内蒙古雷达技术与应用重点实验室）
A Large-Scale Remote Sensing Dataset and VLM-based Algorithm for Fine-Grained Road Hierarchy Classification
School of Geospatial Engineering and Science（地理工程与科学学院）；Laboratory of Intelligent Collaborative Computing（智能协同计算实验室）；United Nations University Institute in Macau（联合国澳门研究所）；Moganshan Geospatial Information Laboratory（莫干山地理信息实验室）
The binary segmentation of roads in very high resolution (VHR) remote sensing images (RSIs) has always been a challenging task due to factors such as occlusions (caused by shadows, trees, buildings, etc.) and the intra-class variances of road surfaces. The wide use of convolutional neural networks (CNNs) has greatly improved the segmentation accuracy and made the task end-to-end trainable. However, there are still margins to improve in terms of the completeness and connectivity of the results. In this paper, we consider the specific context of road extraction and present a direction-aware resi…
Wuhan University（武汉大学）；University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）
Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. Deep neural networks have advanced this field by leveraging the power of large-scale labeled data, which, however, are extremely expensive and time-consuming to acquire. One solution is to use cheap available data to train a model and deploy it to directly process the data from a specific application domain. Nevertheless, the well-known domain shift (DS) issue prevents the trained model from generalizing well on the target domain. In this paper, we propose a novel stagewise domain ada…
Shanghai Jiao Tong University（上海交通大学）；University of Leicester（莱斯特大学）；Central South University of Forestry and Technology（中南林业科技大学）；Massey University（梅西大学）
Road extraction in remote sensing images is of great importance for a wide range of applications. Because of the complex background, and high density, most of the existing methods fail to accurately extract a road network that appears correct and complete. Moreover, they suffer from either insufficient training data or high costs of manual annotation. To address these problems, we introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation. We incorporate a feature pyramid network into generative adversarial networks to minimize the difference…
LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images
School of Medical Instruments, Shanghai University of Medicine and Health Sciences（上海健康医学院医疗器械学院）；School of Health Sciences and Engineering, University of Shanghai for Science and Technology（上海理工大学健康科学与工程学院）；Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属上海儿童医学中心）；School of Communication & Electronic Engineering, East China Normal University（华东师范大学通信与电子工程学院）；Institute of Seismology, China Earthquake Administration（中国地震局地震研究所）
Research Institute on Embedded Electronic Systems (IRSEEM)（嵌入式电子系统研究所（IRSEEM））；ESIGELEC School of Engineering（ESIGELEC高等工程学院）；University of the Basque Country（巴斯克大学）；IKERBASQUE Basque Foundation for Science（IKERBASQUE巴斯克科学基金会）
In this paper, we propose a design for novel and experimental cloud computing systems. The proposed system aims at enhancing computational, communicational and annalistic capabilities of road navigation services by merging several independent technologies, namely vision-based embedded navigation systems, prominent Cloud Computing Systems (CCSs) and Vehicular Ad-hoc NETwork (VANET). This work presents our initial investigations by describing the design of a global generic system. The designed system has been experimented with various scenarios of video-based road services. Moreover, the associa…
In this work, we present a novel approach to learning an encoding of visual features into graph neural networks with the application on road network data. We propose an architecture that combines state-of-the-art vision backbone networks with graph neural networks. More specifically, we perform a road type classification task on an Open Street Map road network through encoding of satellite imagery using various ResNet architectures. Our architecture further enables fine-tuning and a transfer-learning approach is evaluated by pretraining on the NWPU-RESISC45 image classification dataset for rem…
Automated road network extraction from remote sensing imagery remains a significant challenge despite its importance in a broad array of applications. To this end, we explore road network extraction at scale with inference of semantic features of the graph, identifying speed limits and route travel times for each roadway. We call this approach City-Scale Road Extraction from Satellite Imagery v2 (CRESIv2), Including estimates for travel time permits true optimal routing (rather than just the shortest geographic distance), which is not possible with existing remote sensing imagery based methods…
The Australian National University（澳大利亚国立大学）；University of California, Merced（加州大学默塞德分校）；Google Research（谷歌研究院）；Yonsei University（延世大学）；Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Monash University（莫纳什大学）
Vision-and-Language Navigation (VLN) requires an agent to find a path to a remote location on the basis of natural-language instructions and a set of photo-realistic panoramas. Most existing methods take the words in the instructions and the discrete views of each panorama as the minimal unit of encoding. However, this requires a model to match different nouns (e.g., TV, table) against the same input view feature. In this work, we propose an object-informed sequential BERT to encode visual perceptions and linguistic instructions at the same fine-grained level, namely objects and words. Our seq…
Building and Road Recognition in Dense Urban Informal Settlements: A Dataset and Benchmark
Guangdong Provincial Project（广东省项目）；Guangzhou-HKUST(GZ) Joint Funding Program（广州-香港科技大学联合基金计划）；AI Research and Learning Base of Urban Culture Project（城市文化AI研究与学习基地项目）
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network which combines the strengths of residual learning and U-Net is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model is two-fold: first, residual units ease training of deep networks. Second, the rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters howev…
Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic o…
Automatically extracting roads from satellite imagery is a fundamental yet challenging computer vision task in the field of remote sensing. Pixel-wise semantic segmentation-based approaches and graph-based approaches are two prevailing schemes. However, prior works show the imperfections that semantic segmentation-based approaches yield road graphs with low connectivity, while graph-based methods with iterative exploring paradigms and smaller receptive fields focus more on local information and are also time-consuming. In this paper, we propose a new scheme for multi-task satellite imagery roa…
Road detection based on remote sensing images is of great significance to intelligent traffic management. The performances of the mainstream road detection methods are mainly determined by their extracted features, whose richness and robustness can be enhanced by fusing features of different types and cross-layer connections. However, the features in the existing mainstream model frameworks are often similar in the same layer by the single-task training, and the traditional cross-layer fusion ways are too simple to obtain an efficient effect, so more complex fusion ways besides concatenation a…
Remote sensing is extensively used in cartography. As transportation networks grow and change, extracting roads automatically from satellite images is crucial to keep maps up-to-date. Synthetic Aperture Radar satellites can provide high resolution topographical maps. However roads are difficult to identify in these data as they look visually similar to targets such as rivers and railways. Most road extraction methods on Synthetic Aperture Radar images still rely on a prior segmentation performed by classical computer vision algorithms. Few works study the potential of deep learning techniques,…
Identification of road networks and optimal routes directly from remote sensing is of critical importance to a broad array of humanitarian and commercial applications. Yet while identification of road pixels has been attempted before, estimation of route travel times from overhead imagery remains a novel problem, particularly for off-nadir overhead imagery. To this end, we extract road networks with travel time estimates from the SpaceNet MVOI dataset. Utilizing the CRESIv2 framework, we demonstrate the ability to extract road networks in various observation angles and quantify performance at …
Sun Yat-Sen University（中山大学）；The Guangdong University of Technology（广东工业大学）
Land remote sensing analysis is a crucial research in earth science. In this work, we focus on a challenging task of land analysis, i.e., automatic extraction of traffic roads from remote sensing data, which has widespread applications in urban development and expansion estimation. Nevertheless, conventional methods either only utilized the limited information of aerial images, or simply fused multimodal information (e.g., vehicle trajectories), thus cannot well recognize unconstrained roads. To facilitate this problem, we introduce a novel neural network framework termed Cross-Modal Message P…
RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction
NKIARI, Shenzhen Futian（深圳福田NKIARI）；VCIP, CS, Nankai University（南开大学VCIP研究所）；AAIS, Nankai University（南开大学AAIS）；College of Electronic Engineering, National University of Defense Technology, Changsha, China（国防科技大学电子工程学院，长沙，中国）
Aerial image analysis at a semantic level is important in many applications with strong potential impact in industry and consumer use, such as automated mapping, urban planning, real estate and environment monitoring, or disaster relief. The problem is enjoying a great interest in computer vision and remote sensing, due to increased computer power and improvement in automated image understanding algorithms. In this paper we address the task of automatic geolocalization of aerial images from recognition and matching of roads and intersections. Our proposed method is a novel contribution in the …
URoadNet: Dual Sparse Attentive U-Net for Multiscale Road Network Extraction
College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications（南京邮电大学自动化学院与人工智能学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Jarvis Research Center, Tencent YouTu Lab（腾讯优图实验室贾维斯研究中心）
Foundational mapping remains a challenge in many parts of the world, particularly in dynamic scenarios such as natural disasters when timely updates are critical. Updating maps is currently a highly manual process requiring a large number of human labelers to either create features or rigorously validate automated outputs. We propose that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques. Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, a…
Automated road network extraction from remote sensing imagery remains a significant challenge despite its importance in a broad array of applications. To this end, we leverage recent open source advances and the high quality SpaceNet dataset to explore road network extraction at scale, an approach we call City-scale Road Extraction from Satellite Imagery (CRESI). Specifically, we create an algorithm to extract road networks directly from imagery over city-scale regions, which can subsequently be used for routing purposes. We quantify the performance of our algorithm with the APLS and TOPO grap…
RSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation
College of Information Science and Technology, Nanjing Forestry University（南京林业大学信息科学技术学院）；Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics（南京航空航天大学深圳研究院）
Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images
College of Computer Science and Software Engineering, Hohai University（河海大学计算机与软件学院）；Hong Kong University of Science and Technology（香港科技大学）；School of Computer and Electronic Information, Nanjing Normal University（南京师范大学计算机与电子信息学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Slender Object Scene Segmentation in Remote Sensing Image Based on Learnable Morphological Skeleton with Segment Anything Model
School of Mathematical Sciences, Beijing Normal University（北京师范大学数学科学学院）；State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University（北京师范大学地理科学学部遥感科学国家重点实验室）
Ulsan National Institute of Science and Technology（蔚山科学技术院）；Satrec Initiative Inc.（Satrec Initiative公司）；SI Analytics Inc.（SI Analytics公司）
Road extraction from very high resolution satellite (VHR) images is one of the most important topics in the field of remote sensing. In this paper, we propose an efficient Non-Local LinkNet with non-local blocks that can grasp relations between global features. This enables each spatial feature point to refer to all other contextual information and results in more accurate road segmentation. In detail, our single model without any post-processing like CRF refinement, performed better than any other published state-of-the-art ensemble model in the official DeepGlobe Challenge. Moreover, our NL-…
Spatial-Frequency Gated Swin Transformer for Cross-Sensor Remote Sensing Super-Resolution
Space Applications Centre, ISRO, Ahmedabad, India（印度航天研究组织阿赫迈德亚布德研究中心）；Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, India（印度理工学院孟买资源工程研究学院）；New L J Institute of Engineering and Technology, Ahmedabad, India（阿赫迈德亚布德新LJ工程与技术学院）；Pandit Deendayal Energy University, Gandhinagar, India（潘迪特·德恩达尔能源大学）；GLS University, Ahmedabad, India（阿赫迈德亚布德GLS大学）
DSFC-Net: A Dual-Encoder Spatial and Frequency Co-Awareness Network for Rural Road Extraction
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；Department of Earth System Science, Tsinghua University（清华大学地球系统科学系）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）
Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction
Simon Fraser University（西蒙弗雷泽大学）；Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）；University of Waterloo（滑铁卢大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（亥姆霍兹德累斯顿罗森多夫研究中心（HZDR））；Institute of Advanced Research in Artificial Intelligence (IARAI)（人工智能高级研究院（IARAI））
SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation
College of Instrumentation and Electrical Engineering, Jilin University（吉林大学仪器电气工程学院）；Key Laboratory of Geophysical Exploration Equipment, Ministry of Education（教育部地球物理探测装备重点实验室）；School of Mathematics, Jilin University（吉林大学数学学院）
We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neural networks able to learn scale-specific features. Once chained appropriately, they perform global registration in linear time, getting rid of gradient descent schemes by predicting directly the deformat…
RSHazeDiff: A Unified Fourier-aware Diffusion Model for Remote Sensing Image Dehazing
School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术与产业化协同创新中心）；College of Computer Science and Technology, Anhui University of Technology（安徽工业大学计算机科学与技术学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Norwegian Computing Center（挪威计算中心）；UiT, the Arctic University of Norway（挪威北极大学）
We propose a network for semantic mapping called the Dense Dilated Convolutions Merging Network (DDCM-Net) to provide a deep learning approach that can recognize multi-scale and complex shaped objects with similar color and textures, such as buildings, surfaces/roads, and trees in very high resolution remote sensing images. The proposed DDCM-Net consists of dense dilated convolutions merged with varying dilation rates. This can effectively enlarge the kernels' receptive fields, and, more importantly, obtain fused local and global context information to promote surrounding discriminative capabi…
