{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T17:13:31Z","timestamp":1770743611710,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T00:00:00Z","timestamp":1631664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Construction Project of China Knowledge Center for Engineering Sciences and Technology","award":["Grant NO. CKCEST-2021-2-18"],"award-info":[{"award-number":["Grant NO. CKCEST-2021-2-18"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["Grant NO. XDA20030302"],"award-info":[{"award-number":["Grant NO. XDA20030302"]}]},{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["Grant No. XDA20020402"],"award-info":[{"award-number":["Grant No. XDA20020402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Heat waves may negatively impact the economy and human life under global warming. The use of air conditioners can reduce the vulnerability of humans to heat wave disasters. However, air conditioner usage has been not clear until now. Traditional registration investigation methods are cumbersome and require expensive labor and time. This study used a Labelme image tagging tool and an available street view images database to firstly establish a monographic dataset to detect external air conditioner unit features and proposed two deep learning algorithms of Mask-RCNN and YOLOv5 to automatically retrieve air conditioners. The training dataset used street view images in the 2nd Ring Road area of downtown Beijing. The model evaluation mAP of Mask-RCNN and YOLOv5 reached 0.99 and 0.9428. In comparison, the performance of YOLOv5 was superior, which is attributed to the YOLOv5 model being better at detecting smaller target entities equipped with a lighter network structure and an enhanced feature extraction network. We demonstrated the feasibility of using street view images to retrieve air conditioners and showed their great potential to detect air conditioners in the future.<\/jats:p>","DOI":"10.3390\/rs13183691","type":"journal-article","created":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T12:00:44Z","timestamp":1631707244000},"page":"3691","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Deep Learning-Based Method for Detection of External Air Conditioner Units from Street View Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Fei","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research of Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research of Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s10546-005-0905-5","article-title":"Development of a multi-layer urban canopy model for the analysis of energy consumption in a big city: Structure of the urban canopy model and its basic performance","volume":"116","author":"Kondo","year":"2005","journal-title":"Boundary-Layer Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1038\/d41586-020-00694-1","article-title":"Simulation of urban high temperature heat wave regulation mechanism","volume":"29","author":"Chen","year":"2020","journal-title":"J. Nat. Disasters"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"137678","DOI":"10.1016\/j.scitotenv.2020.137678","article-title":"Defining heat waves and extreme heat events using sub-regional meteorological data to maximize benefits of early warning systems to population health","volume":"721","author":"McElroy","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s00704-010-0263-1","article-title":"Modeling the impact of urbanization on the local and regional climate in Yangtze River Delta, China","volume":"102","author":"Zhang","year":"2010","journal-title":"Theor. Appl. Climatol."},{"key":"ref_5","first-page":"642","article-title":"Urbanization and surface non-uniform warming in eastern China","volume":"58","author":"Wu","year":"2013","journal-title":"Chin. Sci. Bull."},{"key":"ref_6","first-page":"138","article-title":"Contribution of urbanization to temperature change in Beijing","volume":"18","author":"Si","year":"2009","journal-title":"J. Nat. Disasters"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1097\/EDE.0000000000000375","article-title":"Review article: Vulnerability to heat-related mortality: A systematic review, meta-analysis, and meta-regression analysis","volume":"26","author":"Benmarhnia","year":"2015","journal-title":"Epidemiology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108610","DOI":"10.1016\/j.envres.2019.108610","article-title":"Cardiorespiratory effects of heatwaves: A systematic review and meta-analysis of global epidemiological evidence","volume":"177","author":"Cheng","year":"2019","journal-title":"Environ. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s12940-017-0238-0","article-title":"Effects of extreme temperatures on cardiovascular emergency hospitalizations in a Mediterranean region: A self-controlled case series study","volume":"16","author":"Ponjoan","year":"2017","journal-title":"Environ. Health"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1289\/ehp.11594","article-title":"The 2006 California Heat Wave: Impacts on Hospitalizations and Emergency Department Visits","volume":"117","author":"Knowlton","year":"2009","journal-title":"Environ. Health Perspect."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.asoc.2018.05.018","article-title":"A survey on deep learning techniques for image and video semantic segmentation","volume":"70","author":"Oprea","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"Farabet","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.patrec.2018.07.032","article-title":"Video semantic object segmentation by self-adaptation of DCNN","volume":"112","author":"Park","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_15","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 5). Mask R-CNN, international conference on computer vision. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_20","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_21","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_22","unstructured":"(2020, June 26). yolov5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Maniat, M., Camp, C.V., and Kashani, A.R. (2021). Deep learning-based visual crack detection using Google street view images. Neural Comput. Appl.","DOI":"10.1007\/s00521-021-06098-0"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kang, B., Lee, S., and Zou, S. (2021). Developing Sidewalk Inventory Data Using Street View Images. Sensors, 21.","DOI":"10.3390\/s21093300"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isprsjprs.2021.01.016","article-title":"Mapping trees along urban street networks with deep learning and street-level imagery","volume":"175","author":"Lumnitz","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.isprsjprs.2021.03.020","article-title":"Detecting individual abandoned houses from google street view: A hierarchical deep learning approach","volume":"175","author":"Zou","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dick, K., Charih, F., Woo, J., and Green, J.R. (2020, January 13\u201315). Gas prices of America: The machine-augmented crowd-sourcing era. Proceedings of the 2020 17th Conference on Computer and Robot Vision (CRV), Ottawa, ON, Canada.","DOI":"10.1109\/CRV50864.2020.00029"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3691\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:00:19Z","timestamp":1760166019000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3691"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,15]]},"references-count":29,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183691"],"URL":"https:\/\/doi.org\/10.3390\/rs13183691","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,15]]}}}