AUTOMATIC EXTRACTION OF ROAD CENTERLINES AND EDGE LINES FROM AERIAL IMAGES VIA CNN-BASED REGRESSION Article Swipe
Yancong Wei
,
Xiangyun Hu
,
Mingyu Zhang
,
Yonghao Xu
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-annals-v-2-2020-925-2020
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-annals-v-2-2020-925-2020
Extracting roads from aerial images is a challenging task in the field of remote sensing. Most approaches formulate road extraction as a segmentation problem and use thinning and edge detection to obtain road centerlines and edge lines, which could produce spurs around the extracted centerlines/edge lines. In this study, a novel regression-based method is proposed to extract road centerlines and edge lines directly from aerial images. The method consists of three major steps. First, an end-to-end regression network based on CNN is trained to predict confidence maps for road centerlines and estimate road width. Then, after the CNN predicts the confidence map, non-maximum suppression and road tracking are applied to extract accurate road centerlines and construct road topology. Meanwhile, Road edge lines are generated based on the road width estimated by the CNN. Finally, in order to improve the connectivity of extracted road network, tensor voting is applied to detect road intersections and the detected intersections are used as guidance for the overcome of discontinuities. The experiments conducted on the SpaceNet and DeepGlobe datasets show that our approach achieves better performance than other methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-annals-v-2-2020-925-2020
- https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/925/2020/isprs-annals-V-2-2020-925-2020.pdf
- OA Status
- diamond
- Cited By
- 5
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3047166825
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3047166825Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/isprs-annals-v-2-2020-925-2020Digital Object Identifier
- Title
-
AUTOMATIC EXTRACTION OF ROAD CENTERLINES AND EDGE LINES FROM AERIAL IMAGES VIA CNN-BASED REGRESSIONWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-03Full publication date if available
- Authors
-
Yancong Wei, Xiangyun Hu, Mingyu Zhang, Yonghao XuList of authors in order
- Landing page
-
https://doi.org/10.5194/isprs-annals-v-2-2020-925-2020Publisher landing page
- PDF URL
-
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/925/2020/isprs-annals-V-2-2020-925-2020.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/925/2020/isprs-annals-V-2-2020-925-2020.pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Enhanced Data Rates for GSM Evolution, Segmentation, Aerial image, Computer vision, Pattern recognition (psychology), Edge detection, Feature extraction, Image (mathematics), Image processingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2798925380, https://openalex.org/W2593886839, https://openalex.org/W2412782625, https://openalex.org/W6754852571, https://openalex.org/W1984288883, https://openalex.org/W6687483927, https://openalex.org/W6608365309, https://openalex.org/W6762793124, https://openalex.org/W2073196383, https://openalex.org/W1964038289, https://openalex.org/W2205800717, https://openalex.org/W2163200524, https://openalex.org/W73112891, https://openalex.org/W2004490675, https://openalex.org/W2780861787, https://openalex.org/W2620899671, https://openalex.org/W2138317821, https://openalex.org/W2416190443, https://openalex.org/W2811199523, https://openalex.org/W6682859550, https://openalex.org/W4200169950, https://openalex.org/W2893801697, https://openalex.org/W2955058313, https://openalex.org/W2017980693, https://openalex.org/W2981989409, https://openalex.org/W2804199516, https://openalex.org/W4241468141, https://openalex.org/W203741292, https://openalex.org/W2595964094, https://openalex.org/W2774320778, https://openalex.org/W2949395449, https://openalex.org/W4285719527, https://openalex.org/W2155226776, https://openalex.org/W4289667027, https://openalex.org/W2194775991, https://openalex.org/W4236972485 |
| referenced_works_count | 36 |
| abstract_inverted_index.a | 7, 22, 50 |
| abstract_inverted_index.In | 47 |
| abstract_inverted_index.an | 75 |
| abstract_inverted_index.as | 21, 159 |
| abstract_inverted_index.by | 131 |
| abstract_inverted_index.in | 10, 135 |
| abstract_inverted_index.is | 6, 54, 82, 147 |
| abstract_inverted_index.of | 13, 70, 141, 164 |
| abstract_inverted_index.on | 80, 126, 169 |
| abstract_inverted_index.to | 31, 56, 84, 110, 137, 149 |
| abstract_inverted_index.CNN | 81, 98 |
| abstract_inverted_index.The | 67, 166 |
| abstract_inverted_index.and | 25, 28, 35, 60, 91, 105, 115, 153, 172 |
| abstract_inverted_index.are | 108, 123, 157 |
| abstract_inverted_index.for | 88, 161 |
| abstract_inverted_index.our | 177 |
| abstract_inverted_index.the | 11, 43, 97, 100, 127, 132, 139, 154, 162, 170 |
| abstract_inverted_index.use | 26 |
| abstract_inverted_index.CNN. | 133 |
| abstract_inverted_index.Most | 16 |
| abstract_inverted_index.Road | 120 |
| abstract_inverted_index.edge | 29, 36, 61, 121 |
| abstract_inverted_index.from | 3, 64 |
| abstract_inverted_index.map, | 102 |
| abstract_inverted_index.maps | 87 |
| abstract_inverted_index.road | 19, 33, 58, 89, 93, 106, 113, 117, 128, 143, 151 |
| abstract_inverted_index.show | 175 |
| abstract_inverted_index.task | 9 |
| abstract_inverted_index.than | 182 |
| abstract_inverted_index.that | 176 |
| abstract_inverted_index.this | 48 |
| abstract_inverted_index.used | 158 |
| abstract_inverted_index.Then, | 95 |
| abstract_inverted_index.after | 96 |
| abstract_inverted_index.based | 79, 125 |
| abstract_inverted_index.could | 39 |
| abstract_inverted_index.field | 12 |
| abstract_inverted_index.lines | 62, 122 |
| abstract_inverted_index.major | 72 |
| abstract_inverted_index.novel | 51 |
| abstract_inverted_index.order | 136 |
| abstract_inverted_index.other | 183 |
| abstract_inverted_index.roads | 2 |
| abstract_inverted_index.spurs | 41 |
| abstract_inverted_index.three | 71 |
| abstract_inverted_index.which | 38 |
| abstract_inverted_index.width | 129 |
| abstract_inverted_index.First, | 74 |
| abstract_inverted_index.aerial | 4, 65 |
| abstract_inverted_index.around | 42 |
| abstract_inverted_index.better | 180 |
| abstract_inverted_index.detect | 150 |
| abstract_inverted_index.images | 5 |
| abstract_inverted_index.lines, | 37 |
| abstract_inverted_index.lines. | 46 |
| abstract_inverted_index.method | 53, 68 |
| abstract_inverted_index.obtain | 32 |
| abstract_inverted_index.remote | 14 |
| abstract_inverted_index.steps. | 73 |
| abstract_inverted_index.study, | 49 |
| abstract_inverted_index.tensor | 145 |
| abstract_inverted_index.voting | 146 |
| abstract_inverted_index.width. | 94 |
| abstract_inverted_index.applied | 109, 148 |
| abstract_inverted_index.extract | 57, 111 |
| abstract_inverted_index.images. | 66 |
| abstract_inverted_index.improve | 138 |
| abstract_inverted_index.network | 78 |
| abstract_inverted_index.predict | 85 |
| abstract_inverted_index.problem | 24 |
| abstract_inverted_index.produce | 40 |
| abstract_inverted_index.trained | 83 |
| abstract_inverted_index.Finally, | 134 |
| abstract_inverted_index.SpaceNet | 171 |
| abstract_inverted_index.accurate | 112 |
| abstract_inverted_index.achieves | 179 |
| abstract_inverted_index.approach | 178 |
| abstract_inverted_index.consists | 69 |
| abstract_inverted_index.datasets | 174 |
| abstract_inverted_index.detected | 155 |
| abstract_inverted_index.directly | 63 |
| abstract_inverted_index.estimate | 92 |
| abstract_inverted_index.guidance | 160 |
| abstract_inverted_index.methods. | 184 |
| abstract_inverted_index.network, | 144 |
| abstract_inverted_index.overcome | 163 |
| abstract_inverted_index.predicts | 99 |
| abstract_inverted_index.proposed | 55 |
| abstract_inverted_index.sensing. | 15 |
| abstract_inverted_index.thinning | 27 |
| abstract_inverted_index.tracking | 107 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.DeepGlobe | 173 |
| abstract_inverted_index.conducted | 168 |
| abstract_inverted_index.construct | 116 |
| abstract_inverted_index.detection | 30 |
| abstract_inverted_index.estimated | 130 |
| abstract_inverted_index.extracted | 44, 142 |
| abstract_inverted_index.formulate | 18 |
| abstract_inverted_index.generated | 124 |
| abstract_inverted_index.topology. | 118 |
| abstract_inverted_index.Extracting | 1 |
| abstract_inverted_index.Meanwhile, | 119 |
| abstract_inverted_index.approaches | 17 |
| abstract_inverted_index.confidence | 86, 101 |
| abstract_inverted_index.end-to-end | 76 |
| abstract_inverted_index.extraction | 20 |
| abstract_inverted_index.regression | 77 |
| abstract_inverted_index.centerlines | 34, 59, 90, 114 |
| abstract_inverted_index.challenging | 8 |
| abstract_inverted_index.experiments | 167 |
| abstract_inverted_index.non-maximum | 103 |
| abstract_inverted_index.performance | 181 |
| abstract_inverted_index.suppression | 104 |
| abstract_inverted_index.connectivity | 140 |
| abstract_inverted_index.segmentation | 23 |
| abstract_inverted_index.intersections | 152, 156 |
| abstract_inverted_index.centerlines/edge | 45 |
| abstract_inverted_index.discontinuities. | 165 |
| abstract_inverted_index.regression-based | 52 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.50717921 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |