Matches in SemOpenAlex for { <https://semopenalex.org/work/W3217745064> ?p ?o ?g. }
- W3217745064 endingPage "7982" @default.
- W3217745064 startingPage "7982" @default.
- W3217745064 abstract "Building extraction from remote sensing (RS) images is a fundamental task for geospatial applications, aiming to obtain morphology, location, and other information about buildings from RS images, which is significant for geographic monitoring and construction of human activity areas. In recent years, deep learning (DL) technology has made remarkable progress and breakthroughs in the field of RS and also become a central and state-of-the-art method for building extraction. This paper provides an overview over the developed DL-based building extraction methods from RS images. Firstly, we describe the DL technologies of this field as well as the loss function over semantic segmentation. Next, a description of important publicly available datasets and evaluation metrics directly related to the problem follows. Then, the main DL methods are reviewed, highlighting contributions and significance in the field. After that, comparative results on several publicly available datasets are given for the described methods, following up with a discussion. Finally, we point out a set of promising future works and draw our conclusions about building extraction based on DL techniques." @default.
- W3217745064 created "2021-12-06" @default.
- W3217745064 creator A5000789681 @default.
- W3217745064 creator A5050231499 @default.
- W3217745064 creator A5051954358 @default.
- W3217745064 date "2021-11-29" @default.
- W3217745064 modified "2023-10-18" @default.
- W3217745064 title "Deep Learning-Based Building Extraction from Remote Sensing Images: A Comprehensive Review" @default.
- W3217745064 cites W1984636802 @default.
- W3217745064 cites W2000803298 @default.
- W3217745064 cites W2019038438 @default.
- W3217745064 cites W2020631986 @default.
- W3217745064 cites W2022508996 @default.
- W3217745064 cites W2026665610 @default.
- W3217745064 cites W2089525768 @default.
- W3217745064 cites W2108598243 @default.
- W3217745064 cites W2117539524 @default.
- W3217745064 cites W2151155827 @default.
- W3217745064 cites W2157163776 @default.
- W3217745064 cites W2158778629 @default.
- W3217745064 cites W2276892413 @default.
- W3217745064 cites W2412588858 @default.
- W3217745064 cites W2412782625 @default.
- W3217745064 cites W2416190443 @default.
- W3217745064 cites W2522356675 @default.
- W3217745064 cites W2546910646 @default.
- W3217745064 cites W2548390752 @default.
- W3217745064 cites W2552440277 @default.
- W3217745064 cites W2592939477 @default.
- W3217745064 cites W2618530766 @default.
- W3217745064 cites W2623490820 @default.
- W3217745064 cites W2762439315 @default.
- W3217745064 cites W2771077876 @default.
- W3217745064 cites W2778539913 @default.
- W3217745064 cites W2782522152 @default.
- W3217745064 cites W2787614951 @default.
- W3217745064 cites W2790741584 @default.
- W3217745064 cites W2795635230 @default.
- W3217745064 cites W2885628263 @default.
- W3217745064 cites W2887469576 @default.
- W3217745064 cites W2888733778 @default.
- W3217745064 cites W2888799854 @default.
- W3217745064 cites W2888889084 @default.
- W3217745064 cites W2891567162 @default.
- W3217745064 cites W2897936062 @default.
- W3217745064 cites W2908320224 @default.
- W3217745064 cites W2912114399 @default.
- W3217745064 cites W2914928371 @default.
- W3217745064 cites W2915731581 @default.
- W3217745064 cites W2924260171 @default.
- W3217745064 cites W2934268922 @default.
- W3217745064 cites W2937933649 @default.
- W3217745064 cites W2938425859 @default.
- W3217745064 cites W2962140336 @default.
- W3217745064 cites W2963153291 @default.
- W3217745064 cites W2963525222 @default.
- W3217745064 cites W2963881378 @default.
- W3217745064 cites W2967087542 @default.
- W3217745064 cites W2982206001 @default.
- W3217745064 cites W2984899327 @default.
- W3217745064 cites W2991441757 @default.
- W3217745064 cites W2991751858 @default.
- W3217745064 cites W2993017798 @default.
- W3217745064 cites W2994434065 @default.
- W3217745064 cites W2996327453 @default.
- W3217745064 cites W2999858079 @default.
- W3217745064 cites W3000086214 @default.
- W3217745064 cites W3011515952 @default.
- W3217745064 cites W3013719693 @default.
- W3217745064 cites W3014060899 @default.
- W3217745064 cites W3015373233 @default.
- W3217745064 cites W3019847943 @default.
- W3217745064 cites W3021057985 @default.
- W3217745064 cites W3022656627 @default.
- W3217745064 cites W3040608330 @default.
- W3217745064 cites W3044310826 @default.
- W3217745064 cites W3048487290 @default.
- W3217745064 cites W3053564872 @default.
- W3217745064 cites W3081299480 @default.
- W3217745064 cites W3086524888 @default.
- W3217745064 cites W3087594737 @default.
- W3217745064 cites W3100521496 @default.
- W3217745064 cites W3111269891 @default.
- W3217745064 cites W3112979587 @default.
- W3217745064 cites W3126435384 @default.
- W3217745064 cites W3148283428 @default.
- W3217745064 cites W3161559204 @default.
- W3217745064 cites W639708223 @default.
- W3217745064 doi "https://doi.org/10.3390/en14237982" @default.
- W3217745064 hasPublicationYear "2021" @default.
- W3217745064 type Work @default.
- W3217745064 sameAs 3217745064 @default.
- W3217745064 citedByCount "24" @default.
- W3217745064 countsByYear W32177450642022 @default.
- W3217745064 countsByYear W32177450642023 @default.
- W3217745064 crossrefType "journal-article" @default.
- W3217745064 hasAuthorship W3217745064A5000789681 @default.
- W3217745064 hasAuthorship W3217745064A5050231499 @default.