YOLOv2 Deep Learning Model and GIS Based Algorithms for Vehicle Tracking

Malaainine, Mohamed El Imame; Lechgar, Hatim; Rhinane, Hassane

doi:10.4236/jgis.2021.134022

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Modern climate and ecological models depend on automatically measuring and communicating sensor networks and big data approaches with the use of artificial intelligence to gain a more comprehensive spatial understanding of the functions, interactions, and development of complex systems [12]. Geographical information systems (GIS) have mostly replaced the use of print media and maps in science and applied areas [13,14], even if they continue to be used in school education. Spatial (GPS) and remote sensing technologies, such as satellite-based or airborne surface scans at any scale [15].…”

Section: Introductionmentioning

confidence: 99%

Modern Technology in Geography Education—Attitudes of Pre-Service Teachers of Geography on Modern Technology

Bengel

Peter

2021

Education Sciences

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In this study, we focus on teachers’ attitudes to compare and evaluate their ability and readiness to implement technology education in geography lessons. First, the lack of suitable measuring instruments for our intent was identified, and we thus attempted to develop the Modern Technology Attitude Index (MTAI) for remedy. An exploratory factor analysis helped to identify three distinguishable dimensions that depict areas of intimidation (INT), loss of control (LOC), and benefits and easement (BAE), with or through modern technology. The scales were then applied to German university students (n = 357). As a result, the pre-service geography teachers (n = 72) showed higher scores on the affinity scale than on the two aversion scales. Their subject-specific interest correlated negatively with intimidation and positively with the perceived benefits and easements of modern technology, while the perceived loss of control showed no significant correlation. This allows for the conclusion that the subject’s technology-related interest has an influence on cognitive and behavioral attitudes, while this is not the case for affective ones. Further, there are indications that the much-discussed gender gap in technology topics might particularly be related to people’s affective attitudes, while cognitive and behavioral dimensions seem not to be affected. Differing results in other studies on whether the gender gap still exists or not could be due to the fact that, in addition to growing social awareness and a generational change, the measuring tools used may have not yet been able to depict a sufficiently diverse range of attitudes.

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Section: Introductionmentioning

confidence: 99%

Modern Technology in Geography Education—Attitudes of Pre-Service Teachers of Geography on Modern Technology

Bengel

Peter

2021

Education Sciences

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“…Existing tests have shown that YOLO's performance is much better than the object detection systems before DPM and R-CNN when using artworks for testing after it is trained on natural images. It indicates that YOLO can learn highly generalized features, which is helpful for this paper to study the learning of the information projected by internal damage to the concrete surface [21]. 3) Prediction of the output: YOLO has two different approaches to predicting the output.…”

Section: Design Of Damage Identification Model Based On Yolomentioning

confidence: 99%

Damage Security Intelligent Identification of Wharf Concrete Structures under Deep Learning and Digital Image Technology

Zhu¹,

Li²,

Zhu³

2023

IJACSA

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Intelligence (AI) technology has quickly developed under the mighty computing power of computers. At this stage, there are many mature non-destructive testing methods in civil engineering, but they are generally only suitable for simple structures and evident damage characteristics. Therefore, it's necessary for us to investigate the damage identification of wharf concrete structures under deep learning and digital image technology. The article propose a damage detection and localization method based on Neural Network (NN) technology in deep learning and Digital Image Correlation (DIC) to identify internal damage to concrete used for wharf construction. Firstly, the identification model of concrete structure is constructed using NN technology. Then, structural damage identification of concrete is further investigated using DIC. Finally, relevant experiments are designed to verify the effect of the model. The results show that: (1) The damage model of concrete structure constructed by NN technology has high convergence and stability and can control the test error well. (2) The image output by the DIC equipment is processed and input into the NN. The errors of the various parameters of different concretes can be within the acceptable range. This paper aims to provide good ideas and references for follow-up structural health monitoring and other topics and has significant engineering application value.

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“…Accurate and up-to-date road network data is critical for land management and disaster response, unpaved road especially in the villages and wooded areas are not easy to generate using traditional techniques [32]. Malaainine et al [34] created a DL model with GIS-based vehicle tracking algorithms. The spatio-temporal GIS (Geographic Information System) trails of moving vehicles were generated using a Convolutional Neural Network (CNN) that processed high-resolution satellite imagery.…”

Section: Traffic and Route Planningmentioning

confidence: 99%

A Brief Review of Recent Developments in the Integration of Deep Learning with GIS

Mohan

M.V.S.S

2022

GaEE

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The interaction of Deep Learning (DL) methods with Geographical Information System (GIS) provides the opportunity to obtain new insights into environmental processes through the spatial, temporal and spectral resolutions as well as data integration. The two technologies may be connected to form a dynamic system that is incredibly well adapted to the evaluation of environmental conditions through the interrelationships of texture, size, pattern, and process. This perspective has acquired popularity in multiple disciplines. GIS is significantly dependant on processors, particularly for 3D calculations, map rendering, and route calculation whereas DL can process huge amounts of data. DL has received a lot of attention recently as a technology with a plethora of promising results. Furthermore, the growing use of DL methods in a variety of disciplines, including GIS, is evident. This study tries to provide a brief overview of the use of DL methods in GIS. This paper introduces the essential DL concepts relevant to GIS, the majority of which have been published in recent years. This research explores remote sensing applications and technologies in areas such as mapping, hydrological modelling, disaster management, and transportation route planning. Finally, conclusions on contemporary framework methodologies and suggestions for further studies are provided.

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YOLOv2 Deep Learning Model and GIS Based Algorithms for Vehicle Tracking

Cited by 4 publications

References 32 publications

Modern Technology in Geography Education—Attitudes of Pre-Service Teachers of Geography on Modern Technology

Modern Technology in Geography Education—Attitudes of Pre-Service Teachers of Geography on Modern Technology

Damage Security Intelligent Identification of Wharf Concrete Structures under Deep Learning and Digital Image Technology

A Brief Review of Recent Developments in the Integration of Deep Learning with GIS

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