We explore methods to detect automobiles in Planet imagery and build a large scale vector field for moving objects. Planet operates two distinct constellations: high-resolution SkySat satellites as well as medium-resolution SuperDove satellites. We show that both static and moving cars can be identified reliably in high-resolution SkySat imagery. We are able to estimate the speed and heading of moving vehicles by leveraging the inter-band displacement (or "rainbow" effect) of moving objects. Identifying cars and trucks in medium-resolution SuperDove imagery is far more difficult, though a similar rainbow effect is observed in these satellites and enables moving vehicles to be detected and vectorized. The frequent revisit of Planet satellites enables the categorization of automobile and truck activity patterns over broad areas of interest and lengthy timeframes.
DZYNE Technologies（DZYNE技术公司）；University of Kentucky（肯塔基大学）
Our goal is to use overhead imagery to understand patterns in traffic flow, for instance answering questions such as how fast could you traverse Times Square at 3am on a Sunday. A traditional approach for solving this problem would be to model the speed of each road segment as a function of time. However, this strategy is limited in that a significant amount of data must first be collected before a model can be used and it fails to generalize to new areas. Instead, we propose an automatic approach for generating dynamic maps of traffic speeds using convolutional neural networks. Our method ope…
Delft University of Technology（代尔夫特理工大学）；Eindhoven University of Technology（埃因霍温理工大学）；Cyclomedia Technology B.V（Cyclomedia科技有限公司）
Object detection and classification of traffic signs in street-view imagery is an essential element for asset management, map making and autonomous driving. However, some traffic signs occur rarely and consequently, they are difficult to recognize automatically. To improve the detection and classification rates, we propose to generate images of traffic signs, which are then used to train a detector/classifier. In this research, we present an end-to-end framework that generates a realistic image of a traffic sign from a given image of a traffic sign and a pictogram of the target class. We propo…
University of Kentucky（肯塔基大学）；Simon Fraser University（西蒙弗雷泽大学）；DZYNE Technologies（DZYNE科技公司）
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 …
Detecting changes on the Earth, such as urban development, deforestation, or natural disaster, is one of the research fields that is attracting a great deal of attention. One promising tool to solve these problems is satellite imagery. However, satellite images require huge amount of storage, therefore users are required to set Area of Interests first, which was not suitable for detecting potential areas for disaster or development. To tackle with this problem, we develop the novel tool, namely GRASP EARTH, which is the simple change detection application based on Google Earth Engine. GRASP EA…
Scalable Dynamic Origin-Destination Demand Estimation Enhanced by High-Resolution Satellite Imagery Data
Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA（土木与环境工程系，卡内基梅隆大学）；Fujitsu Research of America, Pittsburgh, PA（富士通美国研究院）；Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA（Heinz信息学院）
There is an increasing demand for software that automatically detects and classifies mobile targets such as airplanes, cars, and ships in satellite imagery. Applications of such automated target recognition (ATR) software include economic forecasting, traffic planning, maritime law enforcement, and disaster response. This paper describes the extension of a convolutional neural network (CNN) for classification to a sliding window algorithm for detection. It is evaluated on mobile targets of the xView dataset, on which it achieves detection and classification accuracies higher than 95%.
In this paper, we present a ship detection pipeline for low-cost medium resolution satellite optical imagery obtained from ESA Sentinel-2 and Planet Labs Dove constellations. This optical satellite imagery is readily available for any place on Earth and underutilized in the maritime domain, compared to existing solutions based on synthetic-aperture radar (SAR) imagery. We developed a ship detection method based on a state-of-the-art deep-learning-based object detection method which was developed and evaluated on a large-scale dataset that was collected and automatically annotated with the help…
Efficient Cross-Country Data Acquisition Strategy for ADAS via Street-View Imagery
CARIAD SE；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Esslingen University of Applied Sciences（埃森哲应用科学大学）；FZI Research Center for Information Technology（弗劳恩霍夫信息技术研究中心）
Detection and classification of objects in aerial imagery have several applications like urban planning, crop surveillance, and traffic surveillance. However, due to the lower resolution of the objects and the effect of noise in aerial images, extracting distinguishing features for the objects is a challenge. We evaluate CenterNet, a state of the art method for real-time 2D object detection, on the VisDrone2019 dataset. We evaluate the performance of the model with different backbone networks in conjunction with varying resolutions during training and testing.
Technische Hochschule Ingolstadt（英戈尔施塔特工业大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
The availability of real-world data is a key element for novel developments in the fields of automotive and traffic research. Aerial imagery has the major advantage of recording multiple objects simultaneously and overcomes limitations such as occlusions. However, there are only few data sets available. This work describes a process to estimate a precise vehicle position from aerial imagery. A robust object detection is crucial for reliable results, hence the state-of-the-art deep neural network Mask-RCNN is applied for that purpose. Two training data sets are employed: The first one is optimi…
DeepUrban: Interaction-Aware Trajectory Prediction and Planning for Automated Driving by Aerial Imagery
Department of Electrical Engineering and Information Technology, Intelligent Vehicles Lab (IVL), Munich University of Applied Science（电气工程与信息学院，智能车辆实验室（IVL），慕尼黑应用技术大学）
ADAPT Centre（ADAPT中心）；School of Computer Science and Statistics, Trinity College Dublin（都柏林圣三一大学计算机科学与统计学学院）
Many applications such as autonomous navigation, urban planning and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper we propose to automatically detect and compute the GPS coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov Random Field model…
German Aerospace Center (DLR)（德国航空航天中心（DLR））；Technical University of Munich（慕尼黑工业大学）
Multi-class vehicle detection from airborne imagery with orientation estimation is an important task in the near and remote vision domains with applications in traffic monitoring and disaster management. In the last decade, we have witnessed significant progress in object detection in ground imagery, but it is still in its infancy in airborne imagery, mostly due to the scarcity of diverse and large-scale datasets. Despite being a useful tool for different applications, current airborne datasets only partially reflect the challenges of real-world scenarios. To address this issue, we introduce E…
Veeva Systems（Veeva系统公司）；Level5 Engineering Center, Lyft Inc（Lyft公司Level5工程中心）；Massachusetts Institute of Technology（麻省理工学院）
Analysis of high-resolution satellite images has been an important research topic for traffic management, city planning, and road monitoring. One of the problems here is automatic and precise road extraction. From an original image, it is difficult and computationally expensive to extract roads due to presences of other road-like features with straight edges. In this paper, we propose an approach for automatic road extraction based on a fully convolutional neural network of U-net family. This network consists of ResNet-34 pre-trained on ImageNet and decoder adapted from vanilla U-Net. Based on…
This paper proposes an approach to detect moving objects in Wide Area Motion Imagery (WAMI), in which the objects are both small and well separated. Identifying the objects only using foreground appearance is difficult since a $100-$pixel vehicle is hard to distinguish from objects comprising the background. Our approach is based on background subtraction as an efficient and unsupervised method that is able to output the shape of objects. In order to reliably detect low contrast and small objects, we configure the background subtraction to extract foreground regions that might be objects of in…
Aircraft Detection in Satellite Imagery using Deep Learning Object Detectors
Department of Information Technology School of Computational and Physical AB Technologies（信息科技系 计算与物理AB技术学院）；Maulana Abul Kalam Azad University of Sciences Nagercoil, Kanyakumari, Technology, Kristu Jayanti (Deemed to be University) Tamil Nadu, India（马拉那阿布·卡拉姆·阿扎德科学大学纳杰科尔, 古里安, 技术, 克里斯图·贾亚尼大学（被认定为大学）泰米尔纳德邦, 印度）
German Aerospace Center (DLR)（德国航空航天中心（DLR））；Technical University of Munich（慕尼黑工业大学）
The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lane-wise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to ach…
German Aerospace Center（德国航空航天中心）；Technical University of Munich（慕尼黑工业大学）
Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic monitoring and disaster management. The huge variation in object scale, orientation, category, and complex backgrounds, as well as the different camera sensors pose great challenges for current algorithms. In this work, we propose a new method consisting of a novel joint image cascade and feature pyramid network with multi-size convolution kernels to extract multi-scale strong and weak semantic features. These features are fed into rotation-b…
A Large-Scale Dataset and a New Method for RemoteSensing Traffic Object Segmentation
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能与光学电子学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Software, Northwestern Polytechnical University（西北工业大学软件学院）
Accurate and robust detection of multi-class objects in optical remote sensing images is essential to many real-world applications such as urban planning, traffic control, searching and rescuing, etc. However, state-of-the-art object detection techniques designed for images captured using ground-level sensors usually experience a sharp performance drop when directly applied to remote sensing images, largely due to the object appearance differences in remote sensing images in term of sparse texture, low contrast, arbitrary orientations, large scale variations, etc. This paper presents a novel o…
Assessing the Potential of PlanetScope Satellite Imagery to Estimate Particulate Matter Oxidative Potential
Univ. Grenoble Alpes（格勒诺布尔大学）；CNRS（法国国家科学研究中心）；INRAE（法国国家农业、食品与环境研究院）；IRD（法国发展研究院）；Grenoble INP（格勒诺布尔理工学院）；IGE（地球物理与环境科学研究所）；Data Science Experts（数据科学专家公司）；Duke University（杜克大学）；Duke Kunshan University（昆山杜克大学）；Duke Global Health Institute（杜克全球健康研究所）；Department of Biostatistics and Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Inria（法国国家信息与自动化研究所）；LJK（让·昆诺实验室）
Nanyang Technological University（南洋理工大学）；Continental-NTU Corporate Lab（大陆集团-南洋理工大学联合实验室）；Agency for Science, Technology and Research (ASTAR)（新加坡科技研究局）；Institute for Infocomm Research (I2R)（资讯通信研究院）
Accurate vehicle type classification serves a significant role in the intelligent transportation system. It is critical for ruler to understand the road conditions and usually contributive for the traffic light control system to response correspondingly to alleviate traffic congestion. New technologies and comprehensive data sources, such as aerial photos and remote sensing data, provide richer and high-dimensional information. Also, due to the rapid development of deep neural network technology, image based vehicle classification methods can better extract underlying objective features when p…
Microsoft AI for Good Research Lab（微软人工智能造福社会研究实验室）；Stanford RegLab（斯坦福监管实验室）
Longitudinal studies are vital to understanding dynamic changes of the planet, but labels (e.g., buildings, facilities, roads) are often available only for a single point in time. We propose a general model, Temporal Cluster Matching (TCM), for detecting building changes in time series of remotely sensed imagery when footprint labels are observed only once. The intuition behind the model is that the relationship between spectral values inside and outside of building's footprint will change when a building is constructed (or demolished). For instance, in rural settings, the pre-construction are…
Vehicle speed dataset for the major European road network derived from Sentinel-2 imagery, 2022-2026
Heidelberg Institute for Geoinformation Technology (HeiGIT)（海德堡地理信息技术研究所）；Faculty of Geographical Sciences, University of Lodz（罗兹大学地理科学学院）；GIScience Research Group, Institute of Geography, University of Heidelberg（海德堡大学地理研究所地理信息科学研究组）
Semantic segmentation of satellite imagery is a common approach to identify patterns and detect changes around the planet. Most of the state-of-the-art semantic segmentation models are trained in a fully supervised way using Convolutional Neural Network (CNN). The generalization property of CNN is poor for satellite imagery because the data can be very diverse in terms of landscape types, image resolutions, and scarcity of labels for different geographies and seasons. Hence, the performance of CNN doesn't translate well to images from unseen regions or seasons. Inspired by Conditional Generati…
University of Kaiserslautern（凯泽斯劳滕大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
The increased availability of high resolution satellite imagery allows to sense very detailed structures on the surface of our planet. Access to such information opens up new directions in the analysis of remote sensing imagery. However, at the same time this raises a set of new challenges for existing pixel-based prediction methods, such as semantic segmentation approaches. While deep neural networks have achieved significant advances in the semantic segmentation of high resolution images in the past, most of the existing approaches tend to produce predictions with poor boundaries. In this pa…
University of Oxford（牛津大学）；University of Valencia（瓦伦西亚大学）
High resolution remote sensing imagery is used in broad range of tasks, including detection and classification of objects. High-resolution imagery is however expensive, while lower resolution imagery is often freely available and can be used by the public for range of social good applications. To that end, we curate a multi-spectral multi-image super-resolution dataset, using PlanetScope imagery from the SpaceNet 7 challenge as the high resolution reference and multiple Sentinel-2 revisits of the same imagery as the low-resolution imagery. We present the first results of applying multi-image s…
A robust and fast automatic moving object detection and tracking system is essential to characterize target object and extract spatial and temporal information for different functionalities including video surveillance systems, urban traffic monitoring and navigation, robotic. In this dissertation, I present a collaborative Spatial Pyramid Context-aware moving object detection and Tracking system. The proposed visual tracker is composed of one master tracker that usually relies on visual object features and two auxiliary trackers based on object temporal motion information that will be called …
Sub-metre Lunar DEM Generation and Validation from Chandrayaan-2 OHRC Multi-View Imagery Using an Open-Source Pipeline
School of Computer Science and Engineering Manipal University（曼海姆大学计算机科学与工程学院）；Space Applications Centre (SAC) Indian Space Research Organisation (ISRO)（空间应用中心（SAC）印度空间研究组织（ISRO））；Intelligent Robotics Group NASA Ames Research Center（智能机器人小组美国国家航空航天局阿姆斯研究中心）
Microsoft AI for Good Research Lab（微软AI向善研究实验室）；World Bank（世界银行）
Innovations in computer vision algorithms for satellite image analysis can enable us to explore global challenges such as urbanization and land use change at the planetary level. However, domain shift problems are a common occurrence when trying to replicate models that drive these analyses to new areas, particularly in the developing world. If a model is trained with imagery and labels from one location, then it usually will not generalize well to new locations where the content of the imagery and data distributions are different. In this work, we consider the setting in which we have a singl…
Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-type maps using satellite imagery for precision agriculture
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）
The Interstate-24 MObility Technology Interstate Observation Network (I-24 MOTION) is a new instrument for traffic science located near Nashville, Tennessee. I-24 MOTION consists of 276 pole-mounted high-resolution traffic cameras that provide seamless coverage of approximately 4.2 miles I-24, a 4-5 lane (each direction) freeway with frequently observed congestion. The cameras are connected via fiber optic network to a compute facility where vehicle trajectories are extracted from the video imagery using computer vision techniques. Approximately 230 million vehicle miles of travel occur within…
Road network and building footprint extraction is essential for many applications such as updating maps, traffic regulations, city planning, ride-hailing, disaster response \textit{etc}. Mapping road networks is currently both expensive and labor-intensive. Recently, improvements in image segmentation through the application of deep neural networks has shown promising results in extracting road segments from large scale, high resolution satellite imagery. However, significant challenges remain due to lack of enough labeled training data needed to build models for industry grade applications. I…
High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels
Department of Civil Engineering Florida State University Tallahassee, FL, USA；Dept. of Biosystems \& Agricultural Engineering Michigan State University East Lansing, MI, USA
VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
Qatar Computing Research Institute, Hamad Bin Khalifa University（哈马德·本·哈利法大学卡塔尔计算研究所）；College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院）；Saarland Informatics Campus, Saarland University（萨尔兰大学萨尔兰信息学园区）
SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery
Microsoft Research New England（微软研究院新英格兰分部）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Harvard University（哈佛大学）；Microsoft AI for Good Research Lab（微软AI公益研究实验室）；Wageningen University（瓦赫宁根大学）
Element AI；Mila；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views. This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery. To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end…
