Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping

Shao, Zhenfeng; Cheng, Gui; Li, Deren; Huang, Xiao; Lü, Zhipeng; Liu, Jian

doi:10.1080/10095020.2021.1961567

Cited by 16 publications

(6 citation statements)

References 36 publications

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“…One of the possible solutions is to take advantage of the low-altitude UAV and street view data or OpenStreetMap (OSM). A spatio-temporal-spectral-angular observation model [95] is proposed, which integrates observations from UAV and mobile mapping vehicle platforms to identify precise impervious surface boundaries. The OSM road network can be matched and corrected with the street trees in the high-resolution remote sensing imagery, e.g., morphological feature-oriented algorithm [96], which successfully eliminates the obscuring effects and mitigates the underestimation of impervious surfaces.…”

Section: Vegetation Covermentioning

confidence: 99%

Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Shao

Cheng

et al. 2023

Remote Sensing

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Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces.

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Section: Vegetation Covermentioning

confidence: 99%

Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Shao

Cheng

et al. 2023

Remote Sensing

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“…(Adamopoulos, 2020). According to (Shao, Zhenfeng 2021), Theoretically, both UAV platforms and mobile mapping vehicle platforms can be equipped with sensors with high-temporal, high-spatial, and high-spectral resolution. Remote sensing is used for urban areas as an observation material achieved by building appropriate models or algorithms based on spatial, spectral, and shape features.…”

Section: Dronementioning

confidence: 99%

“…Fig 2. A Spatio-temporal spectral angular observation model that combines observations from UAVs and vehicle cellular mapping (Shao, Zhenfeng 2021). …”

mentioning

confidence: 99%

Low-Cost LiDAR-GNSS-UAV Technology Development for PT Garam’s Three-Dimensional Stockpile Modelling Needs

Imani,

Cahyadi,

Farid

et al. 2023

E-BPJ

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Unmanned Aerial Vehicles (UAV), Global Navigation Satellite Systems (GNSS), and LiDAR will be combined into one of the newest technologies to cover each other's deficiencies. Surveyors can use UAV, GNSS, and LiDAR multi-sensors to map the stockpile of salt PT Garam, whereas the previous process used manual calculations. LiDAR is a survey tool with a low price, around 999 USD. To minimise operational costs, surveyors can use Low-Cost LiDAR, GNSS, and UAV at around 638 USD. The results of the data obtained are calibrated with pitch, roll, and yaw to get the vertical height of the existing contours.

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“…Utilizing mmWave for localization also yields better accuracy [18,19]. As GPS precision is in the order of a few centimeters, it is an active area of research to capture objects larger than an inch in UAV based mobile mapping systems [20], as well as to correlate mapping with UAV localization using GPS [21].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta

Fernando

2022

Drones

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This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.

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Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping

Abstract: Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping, Geo-spatial Information Science,

Cited by 16 publications

References 36 publications

Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Low-Cost LiDAR-GNSS-UAV Technology Development for PT Garam’s Three-Dimensional Stockpile Modelling Needs

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

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