Accurate Detection of Buildings from Satellite Images using CNN

Femin, Arshitha; Biju, K. S.

doi:10.1109/icecce49384.2020.9179232

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…In [38] presented various examples of measuring space clustering processes for a range of three multispectral images captured over Ariz, Phoenix. In [51], NASA proposed the study at the Centre for Research, the University of Kansas with the cooperation and support of government research agencies and other universities have shown the applicability of satellite imagery in various fields within agriculture, oceanography, and earth sciences. This paper shows how characteristics and features of radar are used to get geo-science information.…”

Section: Advancement In Image Processing Computing Visionmentioning

confidence: 99%

An AI & ML Based Detection & Identification in Remote Imagery: State-of-the-Art

Hashmi¹,

Dwivedi²,

Kumar³

2022

JAMRIS

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Remote Imagery and the allied processing have been a matter of curiosity among researchers for a long, and have achieved various fruitful milestones in various facets. From the near past, due to the entrance and buzz of AI & ML based computing, remotely sensed imagery & allied processing technology are growing rapidly and continuously expanding towards multiple important services- like land surveying, traffic surveillance, and various earth science disciplines. As Artificial intelligence has become the charm of researchers, machine learning and deep learning have been proven as the most commonly used and highly effective techniques for object detection. AI & ML based object segmentation &detection makes this area hot and fond to the researchers again with the opportunities of enhanced accuracy in the same. A vast literature is available from a long past till now related to the development of various techniques proposed in this area. In this paper, we have explored the major techniques of image processing for object detection and identification from remote imagery, including the current research directions and future directives. This literature survey is basically focused on analyzing and summarizing the past and recent work done in this field through a chronological sequence of research done for object detection and identification in remotely sensed imagery.

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Section: Advancement In Image Processing Computing Visionmentioning

confidence: 99%

An AI & ML Based Detection & Identification in Remote Imagery: State-of-the-Art

Hashmi¹,

Dwivedi²,

Kumar³

2022

JAMRIS

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“…In addition to the above-mentioned topics, ML is applied to other relevant problems such as channel modeling [256]- [258], remote sensing [259]- [264], traffic prediction and classification [212], [265]- [267], and anti jamming [268]- [271].…”

Section: B Ml-powered Satellite Networkmentioning

confidence: 99%

“…Overview UAV Satellite Multi ML [199]- [201] SL [207]- [210] UL [207], [208] RL [221]- [224], [234], [235] Resource Allocation [226]- [228], [243]- [251] Beam Hopping [229], [249], [272] IoT [227], [228], [250], [273] MEC [32], [252] HO Management [253] Interference Management [254], [255] Others Channel Modeling [256]- [258] Remote Sensing [259]- [264] Traffic Classification [265]- [267] Anti Jamming [268]- [271] MEC [144], [225], [274], [275] IoT [144], [274], [276] Others [277], [278] 3D Placement and Trajectory Design [58], [231]- [233], [279]-…”

Section: Ml-poweredmentioning

confidence: 99%

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Solanki¹,

Chatzinotas²,

Kodheli³

et al. 2021

Preprint

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Non-terrestrial networks (NTNs) traditionally had certain limited applications. However, the recent technological advancements opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting its relevance to 5G networks and essentially, how it will play a pivotal role for the development of 6G and beyond wireless networks. The survey discusses important features of NTNs integration into TNs by delving into the new range of services and use cases, various architectures, and new approaches being adopted to develop new wireless ecosystem. Our survey includes the major progresses and outcomes from academic research as well as industrial efforts. We first start with introducing the relevant 5G use cases and general integration challenges such as handover and deployment difficulties. Then, we review the NTNs operations in mmWave and their potential for internet of things (IoT). Further, we discuss the significance of mobile edge computing (MEC) and machine learning (ML) in NTNs by reviewing the relevant research works. Furthermore, we also discuss the corresponding higher layer advancements and relevant field trials/prototyping at both academic and industrial levels. Finally, we identify and review 6G and beyond application scenarios, novel architectures, technological enablers, and higher layer aspects pertinent to NTNs integration.

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“…Recently, Zhou et al [165] proposed the use of YOLOv3 [166], [167], a CNNbased object detection algorithm, for vehicle detection in RS images. Others have proposed the use of DL for other object detection tasks, such as, building [168], airplane [169], cloud [170], [171], [172], ship [173], [174], and military target [175] detection. AI has also been applied to segment and restore RS images, e.g., in cloud restorations, during which ground regions shadowed by clouds are restored.…”

Section: H Remote Sensing (Rs)mentioning

confidence: 99%

Artificial Intelligence for Satellite Communication: A Review

Fourati,

Alouini

2021

Preprint

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Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing. This work thus provides a general overview of AI, its diverse sub-fields, and its state-ofthe-art algorithms. Several challenges facing diverse aspects of satellite communication systems are then discussed, and their proposed and potential AI-based solutions are presented. Finally, an outlook of field is drawn, and future steps are suggested.

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Accurate Detection of Buildings from Satellite Images using CNN

Cited by 15 publications

References 13 publications

An AI & ML Based Detection & Identification in Remote Imagery: State-of-the-Art

An AI & ML Based Detection & Identification in Remote Imagery: State-of-the-Art

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Artificial Intelligence for Satellite Communication: A Review

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