All Issue

2023 Vol.58, Issue 4 Preview Page

Research Article

31 August 2023. pp. 395-418
Abstract
References
1
강애띠・강영옥, 2015, "타임라인데이터를 이용한 트위터 사용자의 거주 지역 유추방법," 한국공간정보학회지, 23(3), 69-81. 10.12672/ksis.2015.23.2.069
2
강영옥, 2023, "공간정보와 인공지능: GeoAI발전과 활용사례," 대한공간정보학회 대한국토도시계획학회, 공간정보의 이해와 활용, 푸른길, 서울, 160-202.
3
강영옥・조나혜・박소연・김지연, 2021, "합성곱 신경망을 활용한 SNS사진분류 및 관광객과 거주자의 관광활동분석 분석," 대한지리학회지, 56(3), 247-264.
4
김나경・박미소・정민지・황도현・윤홍주, 2021, "무인항공기 영상과 딥러닝 기반의 의미론적 분할 기법을 활용한 야적퇴비 탐지 연구," 대한원격탐사학회지, 37(3), 367-378.
5
김동은・강영옥, 2019,"LSTM을 활용한 불법주정차 시공간 예측 모델링: 서울시 민원신고 데이터를 중심으로," 대한공간정보학회지, 27(3), 39-47. 10.7319/kogsis.2019.27.3.039
6
김석구, 2017, "드론 영상촬영 응용 및 활용," 방송과 미디어, 22(2), 95-105.
7
김세형・채정우・강주영, 2022, "위성영상 이미지를 활용한 연구동향 및 데이터 셋 리뷰," 스마트미디어저널, 11(1), 17-30.
8
김지연・강영옥, 2022, "거리영상 기반 보행환경의 정성적 평가 예측을 위한 딥러닝 모델 개발," 대한공간정보학회지, 28(4), 127-136.
9
김지연・이도현・이지윤・조주연・강영옥, 2022, "궤적데이터 마이닝 연구동향: 응용분야와 분석방법론을 중심으로," 한국지도학회지, 22(3), 37-57. 10.16879/jkca.2022.22.3.037
10
김형우・김민호・이양원, 2022, "딥러닝을 이용한 원격탐사 영상분석 연구동향," 한국 원격 탐사 학회지, 38(5-3), 819-834.
11
김흥민・박수호・한정익・예건희・장선웅, 2022, "위성 및 드론 영상을 이용한 해안쓰레기 모니터링 기법 개발," 대한원격탐사학회지, 38(6), 1109-1124.
12
류동우, 2019, "Geo-ICT 융합기술의 새로운 기회와 도전: GeoCPS 와 GeoAI," 한국자원공학회지, 56(4), 383-397. 10.32390/ksmer.2019.56.4.383
13
박서희・전준철, 2017, "CCTV영상의 동적 객체 탐지 및 추적 기술동향," 한국인터넷 정보학회, 18(1), 39-43.
14
박성욱・김형우・이수진・윤예슬・김은숙・임종환・이양원, 2018, "고해상도 위성영상과 Fully Convolutional Network 를 활용한 산림재해 피해지 탐지," 한국사진지리학회지, 28(4), 87-101. 10.35149/jakpg.2018.28.4.006
15
박소연・강영옥, 2021, "시멘틱 궤적과 GRU모델을 활용한 개별 관광객의 다음 목적지 예측 모델링," 대한공간정보학회지, 29(4), 27-37. 10.7319/kogsis.2021.29.4.027
16
박소연・김지연・강영옥・조나혜・윤지영, 2020,"SNS사진에 나타난 사용자 선호 기반의 장소추천," 한국공간정보학회지, 22(1), 53-68.
17
박재성・정지호・정진아・김기홍・신재현・이동엽・정새봄, 2022, "딥러닝 기법을 이용한 제주도 중제주수역 지하수위 예측 모델개발," 지질공학, 32(4), 697-723.
18
박지영・강영옥・김지연, 2022,"거리영상과 시멘틱 세그먼테이션을 활용한 보행환경 평가지표 개발," 한국지도학회지, 22(1), 53-68. 10.16879/jkca.2022.22.1.053
19
서준호・양병윤, 2022, "재난지역에서의 신속한 건물 피해 정도 감지를 위한 딥러닝 모델의 정량 평가," 한국측량학회지, 40(5), 381-391.
20
성선경・모준상・나상일・최재완, 2021, "다중분광밴드 위성영상의 작물재배지역 추출을 위한 Attention Gated FC-DenseNet," 대한원격탐사학회지, 37(5), 1061-1070.
21
송아람・최재완・김용일, 2019, "전이학습과 딥러닝 네트워크를 활용한 고해상도 위성영상의 변화탐지," 한국측량학회지, 37(3), 199-208.
22
신형섭・송석호・이동호・박종화, 2021, "무인기 영상 기반 옥수수 재배필지 추출을 위한 Attention U-NET 적용 및 평가," Ecology and Resilient Infrastructure, 8(4), 253- 265.
23
심승보・전찬준・류승기, 2019, "Fast R-CNN을 이용한 도로노면파손 객체 추출 알고리즘 개발," 한국ITS학회 논문지, 18(2), 104-113.
24
윤지영・강영옥, 2021, "CNN기반 다중레이블 전이학습을 통한 관광 이미지 분석," 대한공간정보학회지, 29(4), 15-26. 10.7319/kogsis.2021.29.4.015
25
이대건・조은지・이동천, 2018, "딥러닝을 위한 영역기반 합성곱 신경망에 의한 항공영상에서 건물탐지 평가," 한국측량학회지, 36(6), 469-481.
26
이민재・신상균・김주연・장승수・한상수・최찬호・조우성・이장희・김송현, 2022, "산불의 효과적 진압을 위한 인공지능 및 영상기반 드론 임무제어 시스템," 한국정보기술학회논문지, 20(1), 75-85. 10.14801/jkiit.2022.20.1.75
27
이성혁・이명진, 2020, "위성영상을 활용한 토지피복분류 항목별 딥러닝 최적화 연구," 원격탐사학회지, 36(6-2), 1591-1606.
28
이용준, 2015, "사물인터넷과 공간정보를 융합한 만물인터넷: Geo-IoT," 국토, 5월호, 34-41.
29
이지윤・강영옥, 2023, "벡터 임베딩을 활용한 스마트폰 궤적의 이동모드 분류방법," 2023 대한공간정보학회 춘계학술대회, 35-39.
30
이지윤・강영옥・김지연・박지영, 2022a, "기계학습을 이용한 보행환경 정성적 평가에 영향을 미치는 거리영상 특성 분석," 한국지리학회지, 11(3), 375-391. 10.25202/JAKG.11.3.6
31
이지윤・조주연・김지연・이도현・강영옥, 2022b, "딥러닝을 활용한 비전 기반 궤적 예측 연구 동향 분석," 대한공간정보학회지, 30(4), 113-128. 10.7319/kogsis.2022.30.4.113
32
이창희・윤예린・배세정・어양담・김창재・신상호・박소영・한유경, 2021, "국내학회지 논문리뷰를 통한 원격탐사 분야 딥러닝 연구동향분석," 한국측량학회지, 39(6), 351-370. 10.5209/crla.71324
33
이혜진・강영옥, 2020, "토픽모델링과 LSTM기반 텍스트 분석을 통한 부산방문 외국인 관광객의 선호 관광지 및 관광 매력요인 분석," 한국도시지리학회지, 23(3), 61-70. 10.21189/JKUGS.23.3.5
34
임광혁, 2017, "SNS빅데이터 분석 기술동향 및 발전방향," 한국콘텐츠학회, 15(1), 38-43.
35
장광민, 2021, "고해상도 정사영상을 이용한 딥러닝 기반의 산림수종 분류에 관한 연구," 한국지리정보학회지, 24(3), 1-9.
36
정성호・조효섭・김정엽・이기하, 2018, "딥러닝 기반 LSTM 모형을 이용한 감조하천 수위 예측," 한국수자원학회논문집, 51(12), 1207-1216.
37
정영준・이상익・이종혁・서병훈・김동수・서예진・최원, 2022, "베이지안 딥러닝 기법을 이용한 확률적 적설심 예측 모델 개발," 한국농공학회논문집, 64(6), 35-41.
38
정재원・모혜림・이준형・유영훈・김형수, 2021, "LSTM 기반 딥러닝 기법을 이용한 섬진강 구례교 지점의홍수위 예측," 한국방재학회논문집, 21(3), 193-201.
39
조원호・박기호, 2022, "U-Net을 이용한 딥러닝 기반의 토지피복 변화탐지," 대한지리학회지, 57(3), 297-306.
40
조원호・임용호・박기호, 2019, "합성곱 신경망을 이용한 딥러닝 기반의 토지피복 분류: 한국 토지피복을 대상으로," 대한지리학회지, 54(1), 1-16.
41
차성은・원명수・장근창・김경민・김원국・백승일・임중빈, 2022, "농림위성 활용을 위한 산불 피해지 분류 딥러닝 알고리즘 평가," 대한원격탐사학회지, 38(6), 1273-1283.
42
최성진・김지원・유화평・가동호・여화수, 2019, "딥러닝 기반의 도시 지역 차량궤적 예측 알고리즘 개발 연구," 대한교통학회, 37(5), 422-429. 10.7470/jkst.2019.37.5.422
43
최윤수・김종호・조현철・이창준, 2019, "합성곱 신경망을 이용한 아스팔트 콘크리트 도로포장 표면균열 검출," 한국구조물진단유지관리공학회 논문집, 23(6), 38-44.
44
최혜민・김민규・양현, 2022, "LSTM 을 이용한 한반도 근해 이상수온 예측모델," 대한원격탐사학회지, 38(3), 265-282.
45
허재・박범수・정윤화・정재훈・김병일・한상욱, 2020, "LSTM-RNN 기반 태양광 발전량 추정을 통한 고속도로 주변부 태양광발전 시설의 적지 선별 기술," 대한공간정보학회지, 28(1), 25-33. 10.7319/kogsis.2020.28.1.025
46
홍상연・이석민・임희지・김상일・박희석・황민섭・주재욱・황인창・한영준・양재환・한지혜・윤서연・기현균・이철주, 2021, 서울시 사물인터넷 활용방안, 서울연구원.
47
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L. and Savarese, S., 2016, Social lstm: human trajectory prediction in crowded spaces, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 961-971. 10.1109/CVPR.2016.110
48
Alam, F., Ofli, F., Imran, M., Alam, T. and Qazi, U., 2020, Deep learning benchmarks and datasets for social media image classification for disaster response, 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 151-158. 10.1109/ASONAM49781.2020.938129432418517
49
Alastal, A. I. and Shaqfa, A. H., 2022, GeoAi technologies and their application areas in urban planning and development: concepts, opportunities and challenges in smart city (Kuwait, study case), Journal of Data Analysis and Information Processing, 10(2), 110-126. 10.4236/jdaip.2022.102007
50
Altalak, M., Ammad uddin, M., Alajmi, A. and Rizg, A., 2022, Smart agriculture applications using deep learning technologies: a survey, Applied Sciences, 12, 5919. 10.3390/app12125919
51
Ao, Y., Wang, J., Zhou, M., Lindenbergh, R. C. and Yang, M. Y., 2019, Fully convolutional networks for street furniture identification in panorama images, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W13, 13-20. 10.5194/isprs-archives-XLII-2-W13-13-2019
52
Bai, N., Nourian, P., Luo, R. and Pereira Roders, A., 2022, Heri-graphs: a dataset creation framework for multi-modal machine learning on graphs of heritage values and attributes with social media, ISPRS International Journal of Geo-Information, 11(9), 469. 10.3390/ijgi11090469
53
Bartzokas-Tsiompras, A., Tampouraki, E. M. and Photis, Y. N., 2020, Is walkability equally distributed among downtowners? Evaluating the pedestrian streetscapes of eight European capitals using a micro-scale audit approach, International Journal of Transport Development and Integration, 4, 75-92. 10.2495/TDI-V4-N1-75-92
54
Biljecki, F. and Ito, K., 2021, Street view imagery in urban analytics and GIS: a review, Landscape and Urban Planning, 215, 104217. 10.1016/j.landurbplan.2021.104217
55
Bin, J., Gardiner, B., Li, E. and Liu, Z., 2020, Multi-source urban data fusion for property value assessment: a case study in Philadelphia, Neurocomputing, 404, 70-83. 10.1016/j.neucom.2020.05.013
56
Blečić, I., Cecchini, A. and Trunfio, G. A., 2018, Towards Automatic Assessment of Perceived Walkability, Computational Science and Its Applications - ICCSA 2018, Springer International Publishing, New York. 10.1007/978-3-319-95168-3_24
57
Cai, B. Y., Li, X., Seiferling, I. and Ratti, C., 2018, Treepedia 2.0: applying deep learning for large-scale quantification of urban tree cover, 2018 IEEE International Congress on Big Data, 49-56. 10.1109/BigDataCongress.2018.00014
58
Campbell, A., Both, A. and Sun, Q. C., 2019, Detecting and mapping traffic signs from Google Street View images using deep learning and GIS, Computers, Environment and Urban Systems, 77, 101350. 10.1016/j.compenvurbsys.2019.101350
59
Chacra, D. A. and Zelek, J., 2018, Municipal infrastructure anomaly and defect detection, 2018 26th European Signal Processing Conference, 2125-2129. 10.23919/EUSIPCO.2018.8553322
60
Chen, J., Zhou, C. and Li, F., 2020a, Quantifying the green view indicator for assessing urban greening quality: an analysis based on internet-crawling street view data, Ecological Indicators, 113, 106192. 10.1016/j.ecolind.2020.106192
61
Chen, L. H., Hung, H. M., Sun, C. Y., Wu, E. H. K., Yamaguchi, T., Sato-Shimokawara, E. and Chen, H., 2020b, Trees Detection on Google Street View Images Using Deep Learning and City Open Data, Advances in Intelligent Systems and Computing, Springer International Publishing, New York. 10.1007/978-3-030-39878-1_22
62
Chen, L., Yao, X., Liu, Y., Zhu, Y., Chen, W., Zhao, X. and Chi, T., 2020c, Measuring impacts of urban environmental elements on housing prices based on multisource data - a case study of Shanghai, China, ISPRS International Journal of Geo-Information, 9, 106. 10.3390/ijgi9020106
63
Chen, M., Arribas-Bel, D., and Singleton, A., 2020d, Quantifying the characteristics of the local urban environment through geotagged flickr photographs and image recognition, ISPRS International Journal of Geo-Information, 9, 264. 10.3390/ijgi9040264
64
Cho, N., Kang, Y., Yoon, J., Park, S. and Kim, J., 2022, Classifying tourists' photos and exploring tourism destination image using a deep learning model, Journal of Quality Assurance in Hospitality & Tourism, 23(6), 1480-1508. 10.1080/1528008X.2021.1995567
65
Connealy, N. T., 2020, Understanding the predictors of street robbery hot spots: a matched pairs analysis and systematic social observation, Crime & Delinquency, 67(9), 1319-1352. 10.1177/0011128720926116
66
Corcoran, P. and Spasić, I., 2023, Self-supervised representation learning for geographical data - a systematic literature review, ISPRS International Journal of Geo- Information, 12(2), 64. 10.3390/ijgi12020064
67
Dakin, K., Xie, W., Parkinson, S., Khan, S., Monchuk, L. and Pease, K., 2020, Built environment attributes and crime: an automated machine learning approach, Crime Science, 9(1), 1-17. 10.1186/s40163-020-00122-9
68
Ding, W. and Shen, S., 2019, Online vehicle trajectory prediction using policy anticipation network and optimization-based context reasoning, 2019 International Conference on Robotics and Automation, 9610-9616. 10.1109/ICRA.2019.8793568
69
Ding, W., Chen, J. and Shen, S., 2019, Predicting vehicle behaviors over an extended horizon using behavior interaction network, 2019 International Conference on Robotics and Automation, 8634-8640. 10.1109/ICRA.2019.8794146
70
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G. and Gelly, S., 2020, An image is worth 16x16 words: transformers for image recognition at scale, arXiv, arXiv:2010.11929.
71
Duan, W., Chiang, Y. Y., Leyk, S., Uhl, J. H. and Knoblock, C. A., 2020, Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning, International Journal of Geographical Information Science, 34(4), 824-849. 10.1080/13658816.2019.1698742
72
Dubey, A., Naik, N., Parikh, D., Raskar, R. and Hidalgo, C. A. 2016, Deep learning the city: quantifying urban perception at a global scale, European Conference on Computer Vision, 196-212. 10.1007/978-3-319-46448-0_12
73
Ericsson, L., Gouk, H., Loy, C. C. and Hospedales, T. M., 2022, Self-supervised representation learning: introduction, advances, and challenges, IEEE Signal Processing Magazine, 39(3), 42-62. 10.1109/MSP.2021.3134634
74
Feng, Y., Thiemann, F. and Sester, M., 2019, Learning cartographic building generalization with deep convolutional neural networks, ISPRS International Journal of Geo-Information, 8(6), 258. 10.3390/ijgi8060258
75
Fu, X., Jia, T., Zhang, X., Li, S. and Zhang, Y., 2019, Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning, PLOS ONE, 14, e0217505. 10.1371/journal.pone.021750531145767PMC6542522
76
Geng, X., Li, Y., Wang, L., Zhang, L., Yang, Q., Ye, J. and Liu, Y., 2019, Spatiotemporal multigraph convolution network for ride-hailing demand forecasting, Proceedings of the AAAI Conference on Artificial Intelligence, 33, 3656-3663. 10.1609/aaai.v33i01.33013656
77
Géron, A., 2022, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly Media, Inc., Sebastopol.
78
Gonzalez, D., Rueda-Plata, D., Acevedo, A. B., Duque, J. C., Ramos-Poll'an, R., Betancourt, A. and Garcí, S., 2020, Automatic detection of building typology using deep learning methods on street level images, Building and Environment, 177, 106805. 10.1016/j.buildenv.2020.106805
79
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio,Y., 2014, Generative adversarial nets, Advances in Neural Information Processing Systems, 2672-2680.
80
Goyal, P., Caron, M., Lefaudeux, B., Xu, M., Wang, P., Pai, V., Singh, M., Liptchinsky, V., Misra, I., Joulin, A. and Bojanowski, P., 2021, Self-supervised pretraining of visual features in the wild. arXiv, arXiv:2103.01988.
81
Guan, W., Chen, Z., Feng, F., Liu, W. and Nie, L., 2021, Urban perception: sensing cities via a deep interactive multi-task learning framework, ACM Transactions on Multimedia Computing, Communications, and Applications, 17, 1-20. 10.1145/3424115
82
Gujjar, P. and Vaughan, R., 2019, Classifying pedestrian actions in advance using predicted video of urban driving scenes, 2019 International Conference on Robotics and Automation, 2097-2103. 10.1109/ICRA.2019.8794278
83
Gustat, J., Anderson, C. E., Chukwurah, Q. C., Wallace, M. E., Broyles, S. T. and Bazzano, L. A., 2020, Cross-sectional associations between the neighborhood built environment and physical activity in a rural setting: the bogalusa heart study, BMC Public Health, 20(1), 1-10. 10.1186/s12889-020-09509-432948175PMC7501650
84
Hossain, E., Hoque, M. M., Hoque, E. and Islam, M. S., 2022, A deep attentive multimodal learning approach for disaster identification from social media posts, IEEE Access, 10, 46538-46551. 10.1109/ACCESS.2022.3170897
85
Hu, L., Wu, X., Huang, J., Peng, Y. and Liu, W., 2020, Investigation of clusters and injuries in pedestrian crashes using GIS in Changsha China, Safety Science, 127, 104710. 10.1016/j.ssci.2020.104710
86
Hu, Y., Gao, S., Lunga, D., Li, W., Newsam, S. and Bhadur, B., 2019a, GeoAI at ACM SIGSPATIAL: progress, challenges, and future directions, SIGSPATIAL Special, 11(2), 5-15. 10.1145/3377000.3377002
87
Hu, Y., Li, W., Wright, D., Aydin, O., Wilson, D., Maher, O. and Raad, M., 2019b., Artificial intelligence approaches, arXiv, arXiv:1908.10345. 10.22224/gistbok/2019.3.4
88
Huang, X., Xu, D., Li, Z. and Wang, C., 2020, Translating multispectral imagery to nighttime imagery via conditional generative adversarial networks, IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, 6758-6761. 10.1109/IGARSS39084.2020.9323669
89
Ilic, L., Sawada, M. and Zarzelli, A., 2019, Deep mapping gentrification in a large Canadian city using deep learning and Google Street View, PloS One, 14(3), e0212814. 10.1371/journal.pone.021281430865701PMC6415887
90
Imran, M., Castillo, C., Diaz, F. and Vieweg, S., 2015, Processing social media messages in mass emergency: a survey, ACM Computing Survey, 47(4), 1-38. 10.1145/2771588
91
Ip, A., Irio, L., and Oliveira, R., 2021, Vehicle trajectory prediction based on LSTM recurrent neural networks, 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 1-5. 10.1109/VTC2021-Spring51267.2021.944903834670123PMC8554687
92
Isola, P., Zhu, J.Y., Zhou, T. and Efros, A. A.,2017, Image-to-image translation with conditional adversarial networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5967-5976. 10.1109/CVPR.2017.632
93
Janowicz, K., Gao, S., McKenzie, G., Hu, Y. and Bhaduri, B., 2020, GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond, International Journal of Geographical Information Science, 34(4), 625-636. 10.1080/13658816.2019.1684500
94
Kabir, M. Y. and Madria, S., 2019, A deep learning approach for tweet classification and rescue scheduling for effective disaster management, Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 269-278. 10.1145/3347146.3359097
95
Kang, J., Körner, M., Wang, Y., Taubenböck, H. and Zhu, X. X., 2018, Building instance classification using street view images, ISPRS Journal of Photogrammetry and Remote Sensing, 145, 44-59. 10.1016/j.isprsjprs.2018.02.006
96
Kang, Y., Cho, N., Yoon, J., Park, S. and Kim, J., 2021a, Transfer learning of a deep learning model for exploring tourists' urban image using geotagged photos, ISPRS International Journal of Geo-Information, 10(3), 137. 10.3390/ijgi10030137
97
Kang, Y., Gao, S. and Roth, R. E., 2019, Transferring multiscale map styles using generative adversarial networks, International Journal of Cartography, 5(2-3), 115-141. 10.1080/23729333.2019.1615729
98
Kang, Y., Kim, J., Park, J. and Lee, J., 2023, Assessment of perceived and physical walkability using street view images and deep learning technology, ISPRS International Journal of Geo-Information, 12(5), 186. 10.3390/ijgi12050186
99
Kang, Y., Zhang, F., Gao, S., Lin, H. and Liu, Y., 2020, A review of urban physical environment sensing using street view imagery in public health studies, Annals of GIS, 26, 1-15. 10.1080/19475683.2020.1791954
100
Kang, Y., Zhang, F., Peng, W., Gao, S., Rao, J., Duarte, F. and Ratti, C., 2021b, Understanding house price appreciation using multi-source big geo-data and machine learning, Land Use Policy, 104919. 10.1016/j.landusepol.2020.104919
101
Karras, T., Laine, S. and Aila, T., 2019, A style-based generator architecture for generative adversarial networks, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4401-4410. 10.1109/CVPR.2019.00453
102
Keralis, J. M., Javanmardi, M., Khanna, S., Dwivedi, P., Huang, D., Tasdizen, T. and Nguyen, Q. C., 2020, Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment, BMC Public Health, 20, 1-10. 10.1186/s12889-020-8300-132050938PMC7017447
103
Khusni, U., Dewangkoro, H. I. and Arymurthy, A. M., 2020, Urban area change detection with combining CNN and RNN from Sentinel-2 multispectral remote sensing data, 2020 3rd International Conference on Computer and Informatics Engineering, 171-175. 10.1109/IC2IE50715.2020.9274617
104
Kim, D., Kang, Y., Park, Y., Kim, N. and Lee, J., 2020, Understanding tourists' urban images with geotagged photos using convolutional neural networks, Spatial Information Research, 28(2), 241-255. 10.1007/s41324-019-00285-x
105
Kim, J. and Kang, Y., 2022, Automatic classification of photos by tourist attractions using deep learning model and image feature vector clustering, ISPRS International Journal of Geo-Information, 11(4), 245. 10.3390/ijgi11040245
106
Kim, M. G., Kang, Y. O. and Koh, J. H., 2016, Evaluating residential location inference of twitter users at district level: focused on Seoul city, Spatial Information Research, 24, 493-502. 10.1007/s41324-016-0039-5
107
Kosaraju, V., Sadeghian, A., Martín-Martín, R., Reid, I., Rezatofighi, H. and Savarese, S., 2019, Social-bigat: multimodal trajectory forecasting using bicycle-gan and graph attention networks, Advances in Neural Information Processing Systems, 32.
108
Kumar, A., Abhishek, K., Kumar Singh, A., Nerurkar, P., Chandane, M., Bhirud, S., Patel, D. and Busnel, Y., 2021, Multilabel classification of remote sensed satellite imagery, Transactions on Emerging Telecommunications Technologies, 32, e3988. 10.1002/ett.3988
109
Kwon, H. Y. and Kang, Y. O., 2016, Risk analysis and visualization for detecting signs of flood disaster in Twitter, Spatial Information Research, 24, 127-139. 10.1007/s41324-016-0014-1
110
Lara-Benitez, P., Carranza-Garcia, M. and Riquelme, J. C., 2021, An experimental review on deep learning architectures for time series forecasting, International Journal of Neural Systems, 31, 2130001. 10.1142/S012906572130001133588711
111
Laumer, D., Lang, N., van Doorn, N., Aodha, O. M., Perona, P. and Wegner, J. D., 2020, Geocoding of trees from street addresses and street-level images, ISPRS Journal of Photogrammetry and Remote Sensing, 162, 125-136. 10.1016/j.isprsjprs.2020.02.001
112
Laupheimer, D., Tutzauer, P., Haala, N. and Spicker, M., 2018, Neural networks for the classification of building use from street-view imagery. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2, 177-184. 10.5194/isprs-annals-IV-2-177-2018
113
Law, S. and Neira, M., 2019, An unsupervised approach to geographical knowledge discovery using street level and street network images, Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, 56-65 10.1145/3356471.3365238
114
Law, S., Seresinhe, C. I., Shen, Y. and Gutierrez-Roig, M., 2020, Street-frontage-net: urban image classification using deep convolutional neural networks, International Journal of Geographical Information Science, 34, 681-707. 10.1080/13658816.2018.1555832
115
Lee, H. and Kang, Y., 2021, Mining tourists' destinations and preferences through LSTM-based text classification and spatial clustering using Flickr data, Spatial Information Research, 29, 825-839. 10.1007/s41324-021-00397-3
116
Li, M. and Yao, W., 2020, 3D map system for tree monitoring in Hong Kong using Google Street View imagery and deep learning, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3-2020, 765-772. 10.5194/isprs-annals-V-3-2020-765-2020
117
Li, W. and Hsu, C. Y., 2022, GeoAI for large-scale image analysis and machine vision: recent progress of artificial intelligence in geography, ISPRS International Journal of Geo-Information, 11(7), 385. 10.3390/ijgi11070385
118
Li, X., Ratti, C. and Seiferling, I., 2018a, Quantifying the shade provision of street trees in urban landscape: a case study in Boston, USA, using Google Street View, Landscape and Urban Planning, 169, 81-91. 10.1016/j.landurbplan.2017.08.011
119
Li, Y., Fu, K., Wang, Z., Shahabi, C., Ye, J., and Liu, Y., 2018b, Multi-task representation learning for travel time estimation, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1695-1704. 10.1145/3219819.3220033
120
Li, Y., Zhou, X. and Pan, M., 2022, Graph Neural Networks in Urban Intelligence, Graph Neural Networks: Foundations, Frontiers, and Applications, New York. 10.1007/978-981-16-6054-2_27
121
Liang, Y., Xia, Y., Ke, S., Wang, Y., Wen, Q., Zhang, J., Zheng, Y. and Zimmermann, R., 2022, AirFormer: predicting nationwide air quality in China with transformers, arXiv, arXiv:2211.15979. 10.1609/aaai.v37i12.26676
122
Liu, Y., Liu, X., Gao, S., Gong, L., Kang, C., Zhi, Y., Chi, G. and Shi, L., 2015, Social sensing: a new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 105(3), 512-530. 10.1080/00045608.2015.1018773
123
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T. and Xie, S., 2020, A ConvNet for the 2020s, arXiv, arXiv:2201.03545.
124
Lu, Y., Lu, J., Zhang, S. and Hall, P., 2018, Traffic signal detection and classification in street views using an attention model, Computational Visual Media, 4, 253-266. 10.1007/s41095-018-0116-x
125
Lunga, D., Hu, Y., Newsam, S., Gao, S., Martins, B., Yang, L. and Deng, X., 2022, GeoAI at ACM SIGSPATIAL: the new frontier of geospatial artificial intelligence research, SIGSPATIAL Special, 13(1-3), 21-32. 10.1145/3578484.3578491
126
Manglik, A., Weng, X., Ohn-Bar, E. and Kitanil, K. M., 2019, Forecasting time-to-collision from monocular video: feasibility, dataset, and challenges, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 8081-8088. 10.1109/IROS40897.2019.8967730
127
Min, W., Mei, S., Liu, L., Wang, Y. and Jiang, S., 2019, Multi-task deep relative attribute learning for visual urban perception, IEEE Transactions on Image Processing, 29, 657-669. 10.1109/TIP.2019.293250231398119
128
Mou, L., Bruzzone, L., and Zhu, X. X., 2019, Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery, IEEE Transactions on Geoscience and Remote Sensing, 57(2), 924-935. 10.1109/TGRS.2018.2863224
129
Mouzannar, H., Rizk, Y. and Awad, M., 2018, Damage identification in social media posts using multimodal deep learning, Proceedings of the 15th ISCRAM Conference.
130
Nagata, S., Nakaya, T., Hanibuchi, T., Amagasa, S., Kikuchi, H. and Inoue, S., 2020, Objective scoring of streetscape walkability related to leisure walking: statistical modeling approach with semantic segmentation of Google Street View images, Health & Place, 66, 102428. 10.1016/j.healthplace.2020.10242832977303
131
Nassar, A. S. and Lefevre, S., 2019, Automated mapping of accessibility signs with deep learning from ground-level imagery and open data, 2019 Joint Urban Remote Sensing Event, 1-4. 10.1109/JURSE.2019.8808961PMC7510387
132
Nesoff, E. D., Milam, A. J., Pollack, K. M., Curriero, F. C., Bowie, J. V., Gielen, A. C. and Furr-Holden, D. M., 2018, Novel methods for environmental assessment of pedestrian injury: Creation and validation of the inventory for pedestrian safety infrastructure, Journal of Urban Health, 95, 208-221. 10.1007/s11524-017-0226-229442222PMC5906386
133
Nguyen, D. T., Ofli, F., Imran, M. and Mitra, P., 2017, Damage assessment from social media imagery data during disasters, Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, 569-576. 10.1145/3110025.3110109PMC5361529
134
Nguyen, Q. C., Sajjadi, M., McCullough, M., Pham, M., Nguyen, T. T., Yu, W., Meng, H. W., Wen, M., Li, F., Smith, K. R., Brunisholz, K. and Tasdizen, T., 2018, Neighbourhood looking glass: 360 automated characterisation of the built environment for neighbourhood effects research, Journal of Epidemiology and Community Health, 72, 260-266. 10.1136/jech-2017-20945629335255PMC5868527
135
Novack, T., Vorbeck, L., Lorei, H. and Zipf, A., 2020, Towards detecting building facades with graffiti artwork based on street view images, ISPRS International Journal of Geo-Information, 9(2), 98. 10.3390/ijgi9020098
136
Ofli, F., Alam, F. and Imran, M., 2020, Analysis of social media data using multimodal deep learning for disaster response, arXiv, arXiv:2004.11838.
137
Ren, S., He, K., Girshick, R. and Sun, J., 2015, Faster R-CNN: towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems, 28.
138
Rossi, L., Ajmar, A., Paolanti, M. and Pierdicca, R., 2021, Vehicle trajectory prediction and generation using LSTM models and GANs, Plos One, 16(7), e0253868. 10.1371/journal.pone.025386834197526PMC8248611
139
Said, N., Ahmad, K., Riegler, M., Pogorelov, K., Hassan, L., Ahmad, N. and Conci, N., 2019, Natural disasters detection in social media and satellite imagery: a survey, Multimedia Tools and Applications, 78, 31267-31302. 10.1007/s11042-019-07942-1
140
Santani, D., Ruiz-Correa, S. and Gatica-Perez, D., 2018, Looking south: learning urban perception in developing cities, ACM Transactions on Social Computing, 1(3), 1-23. 10.1145/3224182
141
Schootman, M., Perez, M., Schootman, J., Fu, Q., McVay, A., Margenthaler, J., Colditz, G., Kreuter, M. and Jeffe, D., 2020, Influence of built environment on quality of life changes in African-American patients with non-metastatic breast cancer, Health & Place, 63, 102333. 10.1016/j.healthplace.2020.10233332543424PMC7676919
142
Sefrin, O., Riese, F. M., and Keller, S., 2021, Deep learning for land cover change detection, Remote Sensing, 13(1), 78. 10.3390/rs13010078
143
Seng, D., Zhang, Q., Zhang, X., Chen, G. and Chen, X., 2021, Spatiotemporal prediction of air quality based on LSTM neural network, Alexandria Engineering Journal, 60(2), 2021-2032. 10.1016/j.aej.2020.12.009
144
Shafqat, W. and Byun, Y. C., 2020, A context-aware location recommendation system for tourists using hierarchical LSTM model, Sustainability, 12(10), 4107. 10.3390/su12104107
145
Song, X., Chen, K., Li, X., Sun, J., Hou, B., Cui, Y., Zhang, B., Xiong, G. and Wang, Z., 2020, Pedestrian trajectory prediction based on deep convolutional LSTM network, IEEE Transactions on Intelligent Transportation Systems, 22(6), 3285-3302. 10.1109/TITS.2020.2981118
146
Su, L., and Li, L., 2020, Trajectory prediction based on machine learning, IOP Conference Series: Materials Science and Engineering, 790(1), 012032. 10.1088/1757-899X/790/1/012032
147
Sytsma, V. A., Connealy, N. and Piza, E. L., 2021, Environmental predictors of a drug offender crime script: a systematic social observation of google street view images and CCTV footage, Crime & Delinquency, 67(1), 27-57. 10.1177/0011128720910961
148
Tang, Z., Ye, Y., Jiang, Z., Fu, C., Huang, R. and Yao, D., 2020, A data-informed analytical approach to human-scale greenway planning: Integrating multi-sourced urban data with machine learning algorithms, Urban Forestry & Urban Greening, 56, 126871. 10.1016/j.ufug.2020.126871
149
Tao, M., Sun, G. and Wang, T., 2020, Urban mobility prediction based on LSTM and discrete position relationship model, 2020 16th International Conference on Mobility, Sensing and Networking, 473-478. 10.1109/MSN50589.2020.0008131862367
150
Thirlwell, A. and Arandjelović, O., 2020, Big data driven detection of trees in suburban scenes using visual spectrum eye level photography, Sensors, 20, 3051. 10.3390/s2011305132481523PMC7308893
151
Tokuda, E. K., Cesar, R. M. and Silva, C. T., 2019, Quantifying the presence of graffiti in urban environments, 2019 IEEE International Conference on Big Data and Smart Computing, 1-4. 10.1109/BIGCOMP.2019.8679113
152
Vali, A., Comai, S. and Matteucci, M., 2020, Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review, Remote Sensing, 12(15), 2495. 10.3390/rs12152495
153
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. and Polosukhin, I., 2017, Attention is all you need, Advances in Neural Information Processing Systems, 30.
154
Wang, Q., Zhang, X., Chen, G., Dai, F., Gong, Y. and Zhu, K., 2018, Change detection based on Faster R-CNN for high-resolution remote sensing images, Remote Sensing Letters, 9(10), 923-932. 10.1080/2150704X.2018.1492172
155
Wang, S., Li, Y., Zhang, J., Meng, Q., Meng, L. and Gao, F., 2020, Pm2.5-gnn: a domain knowledge enhanced graph neural network for pm2.5 forecasting, Proceedings of the 28th International Conference on Advances in Geographic Information Systems, 163-166. 10.1145/3397536.3422208
156
Wang, Y., Albrecht, C. M., Braham, N. A., Mou, L. and Zhu, X. X., 2022, Self-supervised learning in remote sensing: a review, arXiv, arXiv:2206.13188. 10.1109/MGRS.2022.3198244
157
Wang, Y., Yin, H., Chen, H., Wo, T., Xu, J. and Zheng, K., 2019, Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1227-1235. 10.1145/3292500.3330877
158
Wu, N., Zhao, X. W., Wang, J. and Pan, D., 2020, Learning effective road network representation with hierarchical graph neural networks, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 6-14. 10.1145/3394486.3403043
159
Xie, Q., Li, D., Yu, Z., Zhou, J. and Wang, J., 2020a, Detecting trees in street images via deep learning with attention module, IEEE Transactions on Instrumentation and Measurement, 69, 5395-5406. 10.1109/TIM.2019.2958580
160
Xie, Y., Cai, J., Bhojwani, R., Shekhar, S. and Knight, J., 2020b, A locally-constrained YOLO framework for detecting small and densely-distributed building footprints, International Journal of Geographical Information Science, 34(4), 777-801. 10.1080/13658816.2019.1624761
161
Xie, Z., Lv, W., Huang, S., Lu, Z., Du, B. and Huang, R., 2019, Sequential graph neural network for urban road traffic speed prediction, IEEE Access. 8, 63349-63358. 10.1109/ACCESS.2019.2915364
162
Xu, C., and Zhao, B., 2018, Satellite Image Spoofing: Creating Remote Sensing Dataset with Generative Adversarial Networks, 10th International Conference on Geographic Information Science, 67:1-67:6.
163
Xu, Y., Yang, Q., Cui, C., Shi, C., Song, G., Han, X. and Yin, Y., 2019, Visual Urban Perception with Deep Semantic-Aware Network, International Conference on Multimedia Modeling, 28-40. 10.1007/978-3-030-05716-9_3
164
Yao, D., Zhang, C., Huang, J. and Bi, J., 2017, Serm: a recurrent model for next location prediction in semantic trajectories, Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2411-2414. 10.1145/3132847.3133056
165
Ye, Y., Richards, D., Lu, Y., Song, X., Zhuang, Y., Zeng, W. and Zhong, T., 2019a, Measuring daily accessed street greenery: a human-scale approach for informing better urban planning practices, Landscape and Urban Planning, 191, Article 103434. 10.1016/j.landurbplan.2018.08.028
166
Ye, Y., Xie, H., Fang, J., Jiang, H. and Wang, D., 2019b, Daily accessed street greenery and housing price: measuring economic performance of human-scale streetscapes via new urban data, Sustainability, 11, 1741. 10.3390/su11061741
167
Yi, J. and Park, J., 2020, Hypergraph convolutional recurrent neural network, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 3366-3376. 10.1145/3394486.340338932632165PMC7338451
168
Yu, Q., Wang, C., McKenna, F., Yu, S. X., Taciroglu, E., Cetiner, B. and Law, K. H., 2020, Rapid visual screening of soft-story buildings from street view images using deep learning classification, Earthquake Engineering and Engineering Vibration, 19, 827-838. 10.1007/s11803-020-0598-2
169
Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A. and Eickhoff, C., 2021, A transformer-based framework for multivariate time series representation learning, Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2114-2124. 10.1145/3447548.3467401
170
Zhang, F., Wu, L., Zhu, D. and Liu, Y., 2019a. Social sensing from street-level imagery: a case study in learning spatio-temporal urban mobility patterns, ISPRS Journal of Photogrammetry and Remote Sensing, 153, 48-58. 10.1016/j.isprsjprs.2019.04.017
171
Zhang, H., Xu, H., Tian, X., Jiang, J. and Ma, J., 2021, Image fusion meets deep learning: a survey and perspective, Information Fusion, 76, 323-336. 10.1016/j.inffus.2021.06.008
172
Zhang, K., Chen, D. and Li, C., 2020a, Tourism. how are tourists different? - reading geo-tagged photos through a deep learning model, Journal of Quality Assurance in Hospitality & Tourism, 21(2), 234-43. 10.1080/1528008X.2019.1653243
173
Zhang, K., Chen, Y. and Li, C., 2019b, Discovering the tourists' behaviors and perceptions in a tourism destination by analyzing photos' visual content with a computer deep learning model: the case of Beijing, Tourism Management. 75, 595-608. 10.1016/j.tourman.2019.07.002
174
Zhang, W., Liu, H., Liu, Y., Zhou, J. and Xiong, H., 2020b, Semi-supervised hierarchical recurrent graph neural network for city-wide parking availability prediction, Proceedings of the AAAI Conference on Artificial Intelligence, 34, 1186-1193. 10.1609/aaai.v34i01.5471
175
Zhang, Y. and Dong, R., 2018, Impacts of street-visible greenery on housing prices: evidence from a hedonic price model and a massive street view image dataset in Beijing, ISPRS International Journal of Geo-Information, 7, 104. 10.3390/ijgi7030104
176
Zhang, Y., Siriaraya, P., Kawai, Y. and Jatowt, A. 2020c. Automatic latent street type discovery from web open data, Information Systems, 92, 101536. 10.1016/j.is.2020.101536
177
Zhao, J., Liu, X., Kuang, Y., Chen, Y. V. and Yang, B., 2018, Deep CNN-based methods to evaluate neighborhood-scale urban valuation through street scenes perception, 2018 IEEE Third International Conference on Data Science in Cyberspace, 20-27. 10.1109/DSC.2018.00012
178
Zhou, Z., Wang, Y., Xie, X., Chen, L. and Liu, H., 2020, Riskoracle: a minute-level citywide traffic accident forecasting framework, Proceedings of the AAAI Conference on Artificial Intelligence, 34, 1258-1265. 10.1609/aaai.v34i01.5480
179
Zhu, J. Y., Park, T., Isola, P. and Efros, A. A., 2017, Unpaired image-to-image translation using cycleconsistent adversarial networks, Proceedings of the IEEE International Conference on Computer Vision, 2223-2232. 10.1109/ICCV.2017.244
Information
  • Publisher :The Korean Geographical Society
  • Publisher(Ko) :대한지리학회
  • Journal Title :Journal of the Korean Geographical Society
  • Journal Title(Ko) :대한지리학회지
  • Volume : 58
  • No :4
  • Pages :395-418
  • Received Date : 2023-06-23
  • Revised Date : 2023-08-04
  • Accepted Date : 2023-08-07