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JournalISSN: 2196-6346

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 

Copernicus Publications
About: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences is an academic journal published by Copernicus Publications. The journal publishes majorly in the area(s): Point cloud & Computer science. It has an ISSN identifier of 2196-6346. It is also open access. Over the lifetime, 2770 publications have been published receiving 24357 citations. The journal is also known as: ISPRS annals & Annals of the photogrammetry, remote sensing and spatial information sciences.

Papers published on a yearly basis

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Journal ArticleDOI
TL;DR: It is hoped this http URL will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.
Abstract: . This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

442 citations

Journal ArticleDOI
TL;DR: The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.
Abstract: . For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community. Researchers were encouraged to submit results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.

379 citations

Journal ArticleDOI
TL;DR: An overview of the global DEM/DSM dataset generation project is introduced, including a summary of ALOS and PRISM, in addition to the global data archive status, and the automatic DSM/ORI processing software and its test processing results are described.
Abstract: . The Japan Aerospace Exploration Agency (JAXA) generated the global digital elevation/surface model (DEM/DSM) and orthorectified image (ORI) using the archived data of the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS, nicknamed "Daichi"), which was operated from 2006 to 2011. PRISM consisted of three panchromatic radiometers that acquired along-track stereo images. It had a spatial resolution of 2.5 m in the nadir-looking radiometer and achieved global coverage, making it a suitable potential candidate for precise global DSM and ORI generation. In the past 10 years or so, JAXA has conducted the calibration of the system corrected standard products of PRISM in order to improve absolute accuracies as well as to validate the high-level products such as DSM and ORI. In this paper, we introduce an overview of the global DEM/DSM dataset generation project, including a summary of ALOS and PRISM, in addition to the global data archive status. It is also necessary to consider data processing strategies, since the processing capabilities of the level 1 standard product and the high-level products must be developed in terms of both hardware and software to achieve the project aims. The automatic DSM/ORI processing software and its test processing results are also described.

355 citations

Journal ArticleDOI
TL;DR: A novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene is proposed and the results provide a prove of concept and demonstrate the usefulness of the method.
Abstract: . driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method.

315 citations

Journal ArticleDOI
TL;DR: In this paper, a high-level possible approach for fusing the individual satellite data sets is presented, where the best possible approach is to merge Level 2 soil moisture data derived from different satellite data records.
Abstract: Soil moisture was recently included in the list of Essential Climate Variables (ECVs) that are deemed essential for IPCC (Intergovernmental Panel on Climate Change) and UNFCCC (United Nations Framework Convention on Climate Change) needs and considered feasible for global observation. ECVs data records should be as long, complete and consistent as possible, and in the case of soil moisture this means that the data record shall be based on multiple data sources, including but not limited to active (scatterometer) and passive (radiometer) microwave observations acquired preferably in the low-frequency microwave range. Among the list of sensors that can be used for this task are the C-band scatterometers on board of the ERS and METOP satellites and the multi-frequency radiometers SMMR, SSM/I, TMI, AMSR-E, and Windsat. Together, these sensors already cover a time period of more than 30 years and the question is how can observations acquired by these sensors be merged to create one consistent data record? This paper discusses on a high-level possible approaches for fusing the individual satellite data. It is argued that the best possible approach for the fusion of the different satellite data sets is to merge Level 2 soil moisture data derived from the individual satellite data records. This approach has already been demonstrated within the WACMOS project (http://wacmos.itc.nl/) funded by European Space Agency (ESA) and will be further improved within the Climate Change Initiative (CCI) programme of ESA (http://www.esa-cci.org/).

308 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
2023156
2022361
2021130
2020397
2019179
2018220