Roadway free-flow speed captures the typical vehicle speed in low traffic conditions. Modeling free-flow speed is an important problem in transportation engineering with applications to a variety of design, operation, planning, and policy decisions of highway systems. Unfortunately, collecting large-scale historical traffic speed data is expensive and time consuming. Traditional approaches for estimating free-flow speed use geometric properties of the underlying road segment, such as grade, curvature, lane width, lateral clearance and access point density, but for many roads such features are unavailable. We propose a fully automated approach, RasterNet, for estimating free-flow speed without the need for explicit geometric features. RasterNet is a neural network that fuses large-scale overhead imagery and aerial LiDAR point clouds using a geospatially consistent raster structure. To support training and evaluation, we introduce a novel dataset combining free-flow speeds of road segments, overhead imagery, and LiDAR point clouds across the state of Kentucky. Our method achieves state-of-the-art results on a benchmark dataset.
The motivation of this paper is to address the problem of registering airborne LiDAR data and optical aerial or satellite imagery acquired from different platforms, at different times, with different points of view and levels of detail. In this paper, we present a robust registration method based on building regions, which are extracted from optical images using mean shift segmentation, and from LiDAR data using a 3D point cloud filtering process. The matching of the extracted building segments is then carried out using Graph Transformation Matching (GTM) which allows to determine a common pat…
University of Cambridge（剑桥大学）；Fondazione E. Mach（埃马努埃莱·马赫基金会）；University of Oxford（牛津大学）；The University of Queensland（昆士兰大学）；Natural England（英国自然署）
Recognising individual trees within remotely sensed imagery has important applications in forest ecology and management. Several algorithms for tree delineation have been suggested, mostly based on locating local maxima or inverted basins in raster canopy height models (CHMs) derived from Light Detection And Ranging (LiDAR) data or photographs. However, these algorithms often lead to inaccurate estimates of forest stand characteristics due to the limited information content of raster CHMs. Here we develop a 3D tree delineation method which uses graph cut to delineate trees from the full 3D LiD…
Ford AV LLC（福特自动驾驶有限责任公司）；Ford Motor Company（福特汽车公司）
This paper presents a localization technique using aerial imagery maps and LIDAR based ground reflectivity for autonomous vehicles in urban environments. Traditional localization techniques using LIDAR reflectivity rely on high definition reflectivity maps generated from a mapping vehicle. The cost and effort required to maintain such prior maps are generally very high because it requires a fleet of expensive mapping vehicles. In this work we propose a localization technique where the vehicle localizes using aerial/satellite imagery, eradicating the need to develop and maintain complex high-de…
Applications based on synergistic integration of optical imagery and LiDAR data are receiving a growing interest from the remote sensing community. However, a misaligned integration between these datasets may fail to fully profit the potential of both sensors. In this regard, an optimum fusion of optical imagery and LiDAR data requires an accurate registration. This is a complex problem since a versatile solution is still missing, especially when considering the context where data are collected at different times, from different platforms, under different acquisition configurations. This paper…
There is much current interest in using multi-sensor airborne remote sensing to monitor the structure and biodiversity of forests. This paper addresses the application of non-parametric image registration techniques to precisely align images obtained from multimodal imaging, which is critical for the successful identification of individual trees using object recognition approaches. Non-parametric image registration, in particular the technique of optimizing one objective function containing data fidelity and regularization terms, provides flexible algorithms for image registration. Using a sur…
The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University（信息工程测绘遥感国家重点实验室（LIESMARS），武汉大学）；Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember（工程技术学院测绘工程系，印度尼西亚技术学院十月份十号学院）；Department of Informatics and Information Technology, College of Natural and Applied Sciences (CoNAS), Sokoine University of Agriculture（信息与信息技术系，索科因农业学院自然与应用科学学院）；Geomatics Department, Tshwane University of Technology（测绘系，塔什韦恩技术大学）；Department of Geography, Environmental Management and Energy Studies, Faculty of Science, University of Johannesburg（地理、环境管理与能源研究系，科学学院，约翰内斯堡大学）
L2RSI: Cross-view LiDAR-based Place Recognition for Large-scale Urban Scenes via Remote Sensing Imagery
Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China（福建智能城市感知与计算重点实验室，厦门大学，中国）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China（多媒体可信感知与高效计算重点实验室，中华人民共和国教育部，厦门大学，中国）；University of Science and Technology of China, China（中国科学技术大学，中国）
An accurate depth map of the environment is critical to the safe operation of autonomous robots and vehicles. Currently, either light detection and ranging (LIDAR) or stereo matching algorithms are used to acquire such depth information. However, a high-resolution LIDAR is expensive and produces sparse depth map at large range; stereo matching algorithms are able to generate denser depth maps but are typically less accurate than LIDAR at long range. This paper combines these approaches together to generate high-quality dense depth maps. Unlike previous approaches that are trained using ground-…
Northwestern University（西北大学）；New York University（纽约大学）；HERE Company（HERE公司）
Autonomous driving car is becoming more of a reality, as a key component,high-definition(HD) maps shows its value in both market place and industry. Even though HD maps generation from LiDAR or stereo/perspective imagery has achieved impressive success, its inherent defects cannot be ignored. In this paper, we proposal a novel method for Highway HD maps modeling using pixel-wise segmentation on satellite imagery and formalized hypotheses linking, which is cheaper and faster than current HD maps modeling approaches from LiDAR point cloud and perspective view imagery, and let it becomes an ideal…
Automatic extraction of buildings in urban scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly with the emergence of LiDAR systems since mid-1990s. However, in reality, this task is still very challenging due to the complexity of building size and shapes, as well as its surrounding environment. Active contour model, colloquially called snake model, which has been extensively used in many applications in computer vision and image processing, is also applied to extract buildings from aerial/satellite imagery. Motivated by the limitatio…
The increasing demand for commodities is leading to changes in land use worldwide. In the tropics, deforestation, which causes high carbon emissions and threatens biodiversity, is often linked to agricultural expansion. While the need for deforestation-free global supply chains is widely recognized, making progress in practice remains a challenge. Here, we propose an automated approach that aims to support conservation and sustainable land use planning decisions by mapping tropical landscapes at large scale and high spatial resolution following the High Carbon Stock (HCS) approach. A deep lear…
HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation
Remote Sensing Technology Institute, German Aerospace Center (DLR)（远程传感技术研究所，德国航天中心）；Global Land Monitoring Group, Helmholtz German Research Centre for Geosciences (GFZ)（全球土地监测组，海德堡德国地理科学研究中心）；Department of Aerospace Engineering, University of the Bundeswehr Munich（航空航天工程系，联邦国防军 Munich 大学）；Chair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM)（地球观测数据科学教授职位（SiPEO），慕尼黑技术大学）
Forest canopy height estimation from satellite RGB imagery using large-scale airborne LiDAR-derived training data and monocular depth estimation
Beijing Normal University（北京师范大学）；CSIRO Environment（英联邦科学与工业研究组织环境部）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Wuhan University（武汉大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Acquiring accurate three-dimensional depth information conventionally requires expensive multibeam LiDAR devices. Recently, researchers have developed a less expensive option by predicting depth information from two-dimensional color imagery. However, there still exists a substantial gap in accuracy between depth information estimated from two-dimensional images and real LiDAR point-cloud. In this paper, we introduce a fusion-based depth prediction method, called FusionMapping. This is the first method that fuses colored imagery and two-dimensional laser scan to estimate depth in-formation. Mo…
Uber Advanced Technologies Group（优步先进技术集团）；University of Toronto（多伦多大学）
In this paper we address the problem of detecting crosswalks from LiDAR and camera imagery. Towards this goal, given multiple LiDAR sweeps and the corresponding imagery, 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. These are then used in combination with road centerlines from freely available maps (e.g., OpenStreetMaps) to solve a structured optimization problem which draws the final crosswalk boundaries. Our experiments over crosswalks in a l…
Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain the real-time pseudo point cloud instead of the laser sensor from the image sensor. We propose an approach to use different depth estimators to obtain pseudo point clouds like LiDAR to obtain better performance. Moreover, the training and validating strategy of the depth estimator has adopted stereo imagery data to estimate more accurate depth estimation as well as point cloud results. O…
IRISA（法国信息与自动化研究所）；Université Bretagne Sud（南布列塔尼大学）；University of Salzburg（萨尔茨堡大学）
It is of interest to localize a ground-based LiDAR point cloud on remote sensing imagery. In this work, we tackle a subtask of this problem, i.e. to map a digital elevation model (DEM) rasterized from aerial LiDAR point cloud on the aerial imagery. We proposed a contrastive learning-based method that trains on DEM and high-resolution optical imagery and experiment the framework on different data sampling strategies and hyperparameters. In the best scenario, the Top-1 score of 0.71 and Top-5 score of 0.81 are obtained. The proposed method is promising for feature learning from RGB and DEM for l…
Classification of Single Tree Decay Stages from Combined Airborne LiDAR Data and CIR Imagery
The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；Bavarian Forest National Park（巴伐利亚森林国家公园）；University of Freiburg（弗莱堡大学）；Inland Norway University of Applied Sciences（挪威内陆应用科学大学）
Co-Registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quilt challenging because the different imaging mechanism causes significant geometric and radiometric distortions between such data. To tackle the problem, this paper proposes an automatic registration method based on structural features and three-dimension (3D) phase correlation. In the proposed method, the LiDAR point cloud data is first transformed into the intensity map, which is used as the reference image. Then, we employ the Fast operator to extract uniformly distributed interest points in the aerial imag…
DFKI – German Research Center for Artificial Intelligence（德国人工智能研究中心）；BMW Group（宝马集团）
We propose a new approach called LiDAR-Flow to robustly estimate a dense scene flow by fusing a sparse LiDAR with stereo images. We take the advantage of the high accuracy of LiDAR to resolve the lack of information in some regions of stereo images due to textureless objects, shadows, ill-conditioned light environment and many more. Additionally, this fusion can overcome the difficulty of matching unstructured 3D points between LiDAR-only scans. Our LiDAR-Flow approach consists of three main steps; each of them exploits LiDAR measurements. First, we build strong seeds from LiDAR to enhance the…
The Alan Turing Institute（艾伦·图灵研究所）；University College London（伦敦大学学院）
Information on urban tree canopies is fundamental to mitigating climate change [1] as well as improving quality of life [2]. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-truth and then trains a multi-task machine learning model to generate reliable estimates of tree cover and canopy height in urban areas using multi-source multi-spectral satellite imagery for the case study of Chicago.
Monocular 3D object detection is one of the most challenging tasks in 3D scene understanding. Due to the ill-posed nature of monocular imagery, existing monocular 3D detection methods highly rely on training with the manually annotated 3D box labels on the LiDAR point clouds. This annotation process is very laborious and expensive. To dispense with the reliance on 3D box labels, in this paper we explore the weakly supervised monocular 3D detection. Specifically, we first detect 2D boxes on the image. Then, we adopt the generated 2D boxes to select corresponding RoI LiDAR points as the weak sup…
3D urban reconstruction of buildings from remotely sensed imagery has drawn significant attention during the past two decades. While aerial imagery and LiDAR provide higher resolution, satellite imagery is cheaper and more efficient to acquire for large scale need. However, the high, orbital altitude of satellite observation brings intrinsic challenges, like unpredictable atmospheric effect, multi view angles, significant radiometric differences due to the necessary multiple views, diverse land covers and urban structures in a scene, small base-height ratio or narrow field of view, all of whic…
University of Stuttgart（斯图加特大学）；Leibniz University Hannover（汉诺威莱布尼茨大学）；University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Delft University of Technology（代尔夫特理工大学）
Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such datasets are quite scarce. Within this paper, we aim to alleviate this issue by introducing a new annotated 3D dataset that is unique in three ways: i) The dataset consists of both an Unmanned Aerial Vehicle (UAV) laser scanning point cloud and a 3D textured mesh. ii) The point cloud features a mean point density of a…
IBM Research（IBM研究院）；German Aerospace Center (DLR)（德国航空航天中心（DLR））
Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere. However, the lack of efficient quantification methods of carbon stored in trees renders it difficult to track the process. We present an approach to estimate the carbon storage in trees based on fusing multi-spectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species -- key attributes to carbon storage quantification. We demonstrate that tree species information and their three-dimensional geometric shapes can be estimated from aerial imagery in order to de…
We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and ma…
