Shaded Route Planning Using Active Segmentation and Identification of Satellite Images

• URL: http://arxiv.org/abs/2407.13689v1
• Date: Thu, 18 Jul 2024 16:57:11 GMT
• Title: Shaded Route Planning Using Active Segmentation and Identification of Satellite Images
• Abstract summary: Vulnerable groups, especially pedestrians and cyclists on sun-exposed sidewalks, motivate the development of a route planning method. This paper is the first to introduce a pipeline that utilizes segmentation foundation models to extract shaded areas from satellite images. These areas are then integrated into a multi-layered road map, enabling users to customize routes based on a balance between distance and shade exposure. This system is already implemented online, with a video demonstration, and will be specifically adapted to assist travelers during the 2024 Olympic Games in Paris.
• Score: 2.2162879952427343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heatwaves pose significant health risks, particularly due to prolonged exposure to high summer temperatures. Vulnerable groups, especially pedestrians and cyclists on sun-exposed sidewalks, motivate the development of a route planning method that incorporates somatosensory temperature effects through shade ratio consideration. This paper is the first to introduce a pipeline that utilizes segmentation foundation models to extract shaded areas from high-resolution satellite images. These areas are then integrated into a multi-layered road map, enabling users to customize routes based on a balance between distance and shade exposure, thereby enhancing comfort and health during outdoor activities. Specifically, we construct a graph-based representation of the road map, where links indicate connectivity and are updated with shade ratio data for dynamic route planning. This system is already implemented online, with a video demonstration, and will be specifically adapted to assist travelers during the 2024 Olympic Games in Paris.

Related papers

• Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse [20.496826407313854]
  Drone-view geo-localization aims to match a query drone image, often captured under adverse weather conditions, against a gallery of geo-tagged satellite images.<n>Weather-induced degradations in the drone view severely exacerbate the intrinsic cross-view domain gap.<n>We introduce GeoFuse, a cross-modal fusion framework that integrates precisely aligned road map tiles with satellite imagery.
  arXiv  Detail & Related papers  (2026-05-14T15:01:22Z)
• Benchmarking Egocentric Visual-Inertial SLAM at City Scale [50.1245744173948]
  This paper introduces a new dataset and benchmark for visual-inertial SLAM with egocentric, multi-modal data.<n>We record hours and kilometers of trajectories through a city center with glasses-like devices equipped with various sensors.<n>We show that state-of-the-art systems developed by academia are not robust to these challenges and we identify components that are responsible for this.
  arXiv  Detail & Related papers  (2025-09-30T17:59:31Z)
• DeepShade: Enable Shade Simulation by Text-conditioned Image Generation [25.13463236406671]
  Current routing systems fail to incorporate shade information due to the difficulty of estimating shades directly from satellite imagery.<n>We build an extensive dataset covering diverse longitude-latitude regions, varying levels of building density, and different urban layouts.<n>We propose the DeepShade, a diffusion-based model designed to learn and synthesize shade variations over time.
  arXiv  Detail & Related papers  (2025-07-16T10:19:12Z)
• Inferring Driving Maps by Deep Learning-based Trail Map Extraction [5.381140012327021]
  High-definition (HD) maps offer extensive and accurate environmental information about the driving scene.<n>Recent trends have moved from offline mapping to online mapping, ensuring availability and actuality of the utilized maps.<n>We propose a novel offline mapping approach that integrates trails - informal routes used by drivers - into the map creation process.
  arXiv  Detail & Related papers  (2025-05-15T13:09:19Z)
• StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting [85.67616000086232]
  StreetSurfGS is first method to employ Gaussian Splatting specifically tailored for scalable urban street scene surface reconstruction. StreetSurfGS utilizes a planar-based octree representation and segmented training to reduce memory costs, accommodate unique camera characteristics, and ensure scalability. To address sparse views and multi-scale challenges, we use a dual-step matching strategy that leverages adjacent and long-term information.
  arXiv  Detail & Related papers  (2024-10-06T04:21:59Z)
• CoolWalks: Assessing the potential of shaded routing for active mobility in urban street networks [0.0]
  Shade from buildings can provide cooling and protection for pedestrians, but the extent of this potential benefit is unknown. We explore the potential for shaded walking, using building footprints and street networks from both synthetic and real cities. Our research identifies the limitations and potential of shade for cool, active travel, and is a first step towards a rigorous understanding of shade provision for sustainable mobility in cities.
  arXiv  Detail & Related papers  (2024-05-02T12:09:08Z)
• Homography Guided Temporal Fusion for Road Line and Marking Segmentation [73.47092021519245]
  Road lines and markings are frequently occluded in the presence of moving vehicles, shadow, and glare. We propose a Homography Guided Fusion (HomoFusion) module to exploit temporally-adjacent video frames for complementary cues. We show that exploiting available camera intrinsic data and ground plane assumption for cross-frame correspondence can lead to a light-weight network with significantly improved performances in speed and accuracy.
  arXiv  Detail & Related papers  (2024-04-11T10:26:40Z)
• TrackletMapper: Ground Surface Segmentation and Mapping from Traffic Participant Trajectories [24.817728268091976]
  TrackletMapper is a framework for annotating ground surface types such as sidewalks, roads, and street crossings from object tracklets. We show that the model can be self-distilled for additional performance benefits by aggregating a ground surface map and projecting it into the camera images. We qualitatively and quantitatively attest our findings on a novel large-scale dataset for mobile robots operating in pedestrian areas.
  arXiv  Detail & Related papers  (2022-09-12T13:43:10Z)
• Runway Extraction and Improved Mapping from Space Imagery [0.0]
  We identify two generative adversarial networks (GANs) that translate reversibly between plausible runway maps and satellite imagery. We experimentally show that the traditional grey-tan map palette is not a required training input but can be augmented by higher contrast mapping palettes. We identify examples of faulty runway maps where the published satellite and mapped runways disagree but an automated update renders the correct map using GANs.
  arXiv  Detail & Related papers  (2021-12-30T03:15:45Z)
• SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving [64.10636296274168]
  Road extraction is an essential step in building autonomous navigation systems. Using just convolution neural networks (ConvNets) for this problem is not effective as it is inefficient at capturing distant dependencies between road segments in the image. We propose a Spatial and Interaction Space Graph Reasoning (SPIN) module which when plugged into a ConvNet performs reasoning over graphs constructed on spatial and interaction spaces projected from the feature maps.
  arXiv  Detail & Related papers  (2021-09-16T03:52:17Z)
• End-to-End Deep Structured Models for Drawing Crosswalks [98.9901717499058]
  We project both inputs onto the ground surface to produce a top down view of the scene. We then leverage convolutional neural networks to extract semantic cues about the location of the crosswalks. Experiments over crosswalks in a large city area show that 96.6% automation can be achieved.
  arXiv  Detail & Related papers  (2020-12-21T18:59:08Z)
• Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
  In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps. This paper presents the further development of a previous approach. The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
  arXiv  Detail & Related papers  (2020-08-09T09:26:30Z)
• Learning Lane Graph Representations for Motion Forecasting [92.88572392790623]
  We construct a lane graph from raw map data to preserve the map structure. We exploit a fusion network consisting of four types of interactions, actor-to-lane, lane-to-lane, lane-to-actor and actor-to-actor. Our approach significantly outperforms the state-of-the-art on the large scale Argoverse motion forecasting benchmark.
  arXiv  Detail & Related papers  (2020-07-27T17:59:49Z)
• Probabilistic Semantic Mapping for Urban Autonomous Driving Applications [1.181206257787103]
  We propose to fuse image and pre-built point cloud map information to perform automatic and accurate labeling of static landmarks such as roads, sidewalks, crosswalks, and lanes. The method performs semantic segmentation on 2D images, associates the semantic labels with point cloud maps to accurately localize them in the world, and leverages the confusion matrix formulation to construct a probabilistic semantic map in bird's eye view from semantic point clouds.
  arXiv  Detail & Related papers  (2020-06-08T19:29:09Z)
• Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks [27.86228863466213]
  We present a simple, unified approach for estimating maps directly from monocular images using a single end-to-end deep learning architecture. We demonstrate the effectiveness of our approach by evaluating against several challenging baselines on the NuScenes and Argoverse datasets.
  arXiv  Detail & Related papers  (2020-03-30T12:39:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.