A review on availability of remote sensing data

Aroma, R. Jenice; Raimond, Kumudha

doi:10.1109/tiar.2015.7358548

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…Satellite images, airborne scanning or field measurements are widely used techniques to acquire data about natural habitats (Hinsley et al, 2002;Varela et al, 2008;Coops et al, 2016). Currently, there are multiple data sources where it is possible to obtain data with low or moderate resolution for large-scale analysis (Raimond, 2015). Nevertheless, precise publicly available data with high resolution are still scarce (Sánchez, 2018).…”

Section: Introductionmentioning

confidence: 99%

Public LiDAR data are an important tool for the detection of saproxylic insect hotspots in Mediterranean forests and their connectivity

Rada

Blanco

Horák

et al. 2022

Forest Ecology and Management

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Section: Introductionmentioning

confidence: 99%

Public LiDAR data are an important tool for the detection of saproxylic insect hotspots in Mediterranean forests and their connectivity

Rada

Blanco

Horák

et al. 2022

Forest Ecology and Management

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“…Common challenges associated with satellite image processing methods have been reported in literature and include the following: image complexity, large image sizes, presence of unwanted artifacts and background information. In addition, satellite sensor variations (such as radiometric resolution, spatial, and spectra) and a change of viewpoint bring tremendous diversities into satellite image representations [143]. Images of the above said different variations could be integrated by fusion techniques to enrich the chosen study area's available information.…”

Section: B Challenges With Remote Sensingmentioning

confidence: 99%

Applications and Challenges of Artificial Intelligence in Space Missions

2024

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Artificial Intelligence (AI) is increasingly finding acceptance in the space community, especially Machine Learning (ML), a subdomain of AI. ML algorithms now find numerous applications in autonomous navigation, spacecraft health monitoring and operational management of satellite constellations. However, a large number of surveys on the applications of AI in space missions can be classified into two categories. The first category suffers from the limitation of being old and not covering some crucial and recent developments in the field; such as the contributions of Deep Learning (DL) and bioinspired AI algorithms. The challenge with the second category lies in its being too detailed with respect to the development and application of specific AI techniques or algorithms. These limitations have necessitated the need to have a concise survey with a wider scope for those interested in the applications and challenges of AI in the space industry, especially those with technical backgrounds in other fields. In this paper, we surveyed the use of traditional AI techniques in various domains of space missions without delving into formal methods. Some bioinspired AI algorithms were also surveyed and their potential application areas highlighted. Unlike similar surveys that focus only on technological challenges, we also addressed some crucial legal drawbacks that emanate from the reliance and use of AI in space. Summarily, while discussing future directions we reviewed some advancements in Internet of Things (IoT) and Blockchain technologies. Our review prioritized three application domains positioned to benefit immensely from the inevitable AI-Blockchain convergence in the space community. These include the Internet of Space Things (IoST), Satellite Communication and Spacecraft Data Security.

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“…The image's road features are extracted from four distinct perspectives [52]. The road features from road images are described as follows:…”

Section: Road Features In Road Extractionmentioning

confidence: 99%

Remote Sensing Images based Road Network Extraction using Deep Learning: A Systematic Review

Sharma,

kumar,

Gupta

2023

Preprint

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Automatic extraction of roads based on data-driven methods is essential for various aspects such as route optimization, traffic management, GPS navigation, disaster management, defense, and security intelligence, etc. Due to the occlusion of trees, vehicles, buildings, etc., it is challenging. A systematic review is proposed in this study to overcome the challenges. More than 214 articles from 2018–2022 are collected using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines from different databases (i.e., Scopus, Web of Science (WoS), IEEE Explorer), and 44 are reviewed. The article selection process is based on keywords like "Deep Learning (DL)," "Road Extraction," "High- Resolution Remote Sensing Images (HRRSI)," etc. The different datasets used by the researchers are also discussed in this study, along with the type of sensors and satellites used to collect HRRSI images. This study aims to provide a proposed solution to the investigations retrieved from the previous research work. After analysis, it is concluded that the factors retrieved from this rigorous analysis can be considered to propose a novel model that can resolve the issue of accurate extraction of roads.

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A review on availability of remote sensing data

Cited by 10 publications

References 15 publications

Public LiDAR data are an important tool for the detection of saproxylic insect hotspots in Mediterranean forests and their connectivity

Public LiDAR data are an important tool for the detection of saproxylic insect hotspots in Mediterranean forests and their connectivity

Applications and Challenges of Artificial Intelligence in Space Missions

Remote Sensing Images based Road Network Extraction using Deep Learning: A Systematic Review

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