Narrow road extraction from high-resolution remote sensing images: SWGE-Net and MSIF-Net Article Swipe
Z. G. Zhao
,
Wu Chen
,
San Jiang
,
Yaxin Li
,
Jingxian Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/10095020.2024.2405017
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/10095020.2024.2405017
Accurate and complete road network extraction plays a critical role in urban planning, street navigation, and emergency response. At present, narrow roads are a main feature in most public road datasets. However, the continuity and boundary completeness of the extraction results for these narrow roads are relatively poor, due to their varied shapes, uneven spatial distribution, and the presence of various interfering elements. To address these issues, this study introduces a novel network, the Self-weighted Global Context Road Extraction Network (SWGE-Net), which integrates a dilate block and an improved coordinate attention mechanism to effectively capture the complex details and spatial information of narrow roads. Furthermore, most public road training datasets often lack labels for very narrow roads, this omission leads to poor extraction results for these roads in test datasets. In order to further improve the extraction capability for unlabeled, extremely narrow roads, this study introduces another network called the Multi-scale Information Fusion Road Extraction Network (MSIF-Net), which uses the same encoders as SWGE-Net and has a special module for merging information at different scales. This module, with a dilate block and pyramid pooling-based decoder, makes the network better at recognizing and combining features of different sizes. Experimental results indicate that SWGE-Net outperforms the baseline network with road IoU scores of 71.57% and 60.67% on the DeepGlobe and CHN6-CUG road datasets, respectively an improvement of 18.51% and 5.40%. Meanwhile, MSIF-Net not only exceeds the baseline in road IoU scores for both datasets, but also achieves the best performance in extracting unlabeled, extremely narrow roads in qualitative experiments.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/10095020.2024.2405017
- OA Status
- gold
- Cited By
- 2
- References
- 47
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4403298024Canonical identifier for this work in OpenAlex
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https://doi.org/10.1080/10095020.2024.2405017Digital Object Identifier
- Title
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Narrow road extraction from high-resolution remote sensing images: SWGE-Net and MSIF-NetWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-09-20Full publication date if available
- Authors
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Z. G. Zhao, Wu Chen, San Jiang, Yaxin Li, Jingxian WangList of authors in order
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https://doi.org/10.1080/10095020.2024.2405017Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1080/10095020.2024.2405017Direct OA link when available
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Net (polyhedron), Extraction (chemistry), Remote sensing, Computer science, Artificial intelligence, Environmental science, Geography, Mathematics, Chromatography, Chemistry, GeometryTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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| referenced_works | https://openalex.org/W2743136947, https://openalex.org/W3021297918, https://openalex.org/W2963881378, https://openalex.org/W2962978395, https://openalex.org/W1923697677, https://openalex.org/W2889301062, https://openalex.org/W4312264252, https://openalex.org/W4387515118, https://openalex.org/W4317603812, https://openalex.org/W2804199516, https://openalex.org/W4220989974, https://openalex.org/W4391365543, https://openalex.org/W2536190202, https://openalex.org/W4385748513, https://openalex.org/W2884281986, https://openalex.org/W3212386989, https://openalex.org/W195372719, https://openalex.org/W3177052299, https://openalex.org/W3137034425, https://openalex.org/W1984288883, https://openalex.org/W4308300881, https://openalex.org/W2995766874, https://openalex.org/W2783572327, https://openalex.org/W3018699024, https://openalex.org/W4200512530, https://openalex.org/W3086017879, https://openalex.org/W1903029394, https://openalex.org/W2789894132, https://openalex.org/W2780861787, https://openalex.org/W2244429148, https://openalex.org/W2963270775, https://openalex.org/W1901129140, https://openalex.org/W3047725879, https://openalex.org/W2982628450, https://openalex.org/W4327620107, https://openalex.org/W2395811491, https://openalex.org/W3134927667, https://openalex.org/W2890554434, https://openalex.org/W4387937868, https://openalex.org/W2774320778, https://openalex.org/W4220717988, https://openalex.org/W4308720144, https://openalex.org/W2560023338, https://openalex.org/W4385347325, https://openalex.org/W2893801697, https://openalex.org/W3150573203, https://openalex.org/W3081260473 |
| referenced_works_count | 47 |
| abstract_inverted_index.a | 7, 23, 70, 83, 166, 178 |
| abstract_inverted_index.At | 18 |
| abstract_inverted_index.In | 130 |
| abstract_inverted_index.To | 63 |
| abstract_inverted_index.an | 87, 222 |
| abstract_inverted_index.as | 162 |
| abstract_inverted_index.at | 172, 189 |
| abstract_inverted_index.in | 10, 26, 127, 235, 248, 254 |
| abstract_inverted_index.of | 37, 59, 101, 194, 210, 224 |
| abstract_inverted_index.on | 214 |
| abstract_inverted_index.to | 49, 92, 120, 132 |
| abstract_inverted_index.IoU | 208, 237 |
| abstract_inverted_index.and | 1, 15, 34, 56, 86, 98, 164, 181, 191, 212, 217, 226 |
| abstract_inverted_index.are | 22, 45 |
| abstract_inverted_index.but | 242 |
| abstract_inverted_index.due | 48 |
| abstract_inverted_index.for | 41, 113, 124, 138, 169, 239 |
| abstract_inverted_index.has | 165 |
| abstract_inverted_index.not | 230 |
| abstract_inverted_index.the | 32, 38, 57, 73, 95, 135, 149, 159, 186, 203, 215, 233, 245 |
| abstract_inverted_index.Road | 77, 153 |
| abstract_inverted_index.This | 175 |
| abstract_inverted_index.also | 243 |
| abstract_inverted_index.best | 246 |
| abstract_inverted_index.both | 240 |
| abstract_inverted_index.lack | 111 |
| abstract_inverted_index.main | 24 |
| abstract_inverted_index.most | 27, 105 |
| abstract_inverted_index.only | 231 |
| abstract_inverted_index.poor | 121 |
| abstract_inverted_index.road | 3, 29, 107, 207, 219, 236 |
| abstract_inverted_index.role | 9 |
| abstract_inverted_index.same | 160 |
| abstract_inverted_index.test | 128 |
| abstract_inverted_index.that | 200 |
| abstract_inverted_index.this | 67, 117, 143 |
| abstract_inverted_index.uses | 158 |
| abstract_inverted_index.very | 114 |
| abstract_inverted_index.with | 177, 206 |
| abstract_inverted_index.block | 85, 180 |
| abstract_inverted_index.leads | 119 |
| abstract_inverted_index.makes | 185 |
| abstract_inverted_index.novel | 71 |
| abstract_inverted_index.often | 110 |
| abstract_inverted_index.order | 131 |
| abstract_inverted_index.plays | 6 |
| abstract_inverted_index.poor, | 47 |
| abstract_inverted_index.roads | 21, 44, 126, 253 |
| abstract_inverted_index.study | 68, 144 |
| abstract_inverted_index.their | 50 |
| abstract_inverted_index.these | 42, 65, 125 |
| abstract_inverted_index.urban | 11 |
| abstract_inverted_index.which | 81, 157 |
| abstract_inverted_index.18.51% | 225 |
| abstract_inverted_index.5.40%. | 227 |
| abstract_inverted_index.60.67% | 213 |
| abstract_inverted_index.71.57% | 211 |
| abstract_inverted_index.Fusion | 152 |
| abstract_inverted_index.Global | 75 |
| abstract_inverted_index.better | 188 |
| abstract_inverted_index.called | 148 |
| abstract_inverted_index.dilate | 84, 179 |
| abstract_inverted_index.labels | 112 |
| abstract_inverted_index.module | 168 |
| abstract_inverted_index.narrow | 20, 43, 102, 115, 141, 252 |
| abstract_inverted_index.public | 28, 106 |
| abstract_inverted_index.roads, | 116, 142 |
| abstract_inverted_index.roads. | 103 |
| abstract_inverted_index.scores | 209, 238 |
| abstract_inverted_index.sizes. | 196 |
| abstract_inverted_index.street | 13 |
| abstract_inverted_index.uneven | 53 |
| abstract_inverted_index.varied | 51 |
| abstract_inverted_index.Context | 76 |
| abstract_inverted_index.Network | 79, 155 |
| abstract_inverted_index.address | 64 |
| abstract_inverted_index.another | 146 |
| abstract_inverted_index.capture | 94 |
| abstract_inverted_index.complex | 96 |
| abstract_inverted_index.details | 97 |
| abstract_inverted_index.exceeds | 232 |
| abstract_inverted_index.feature | 25 |
| abstract_inverted_index.further | 133 |
| abstract_inverted_index.improve | 134 |
| abstract_inverted_index.issues, | 66 |
| abstract_inverted_index.merging | 170 |
| abstract_inverted_index.module, | 176 |
| abstract_inverted_index.network | 4, 147, 187, 205 |
| abstract_inverted_index.pyramid | 182 |
| abstract_inverted_index.results | 40, 123, 198 |
| abstract_inverted_index.scales. | 174 |
| abstract_inverted_index.shapes, | 52 |
| abstract_inverted_index.spatial | 54, 99 |
| abstract_inverted_index.special | 167 |
| abstract_inverted_index.various | 60 |
| abstract_inverted_index.Accurate | 0 |
| abstract_inverted_index.CHN6-CUG | 218 |
| abstract_inverted_index.However, | 31 |
| abstract_inverted_index.MSIF-Net | 229 |
| abstract_inverted_index.SWGE-Net | 163, 201 |
| abstract_inverted_index.achieves | 244 |
| abstract_inverted_index.baseline | 204, 234 |
| abstract_inverted_index.boundary | 35 |
| abstract_inverted_index.complete | 2 |
| abstract_inverted_index.critical | 8 |
| abstract_inverted_index.datasets | 109 |
| abstract_inverted_index.decoder, | 184 |
| abstract_inverted_index.encoders | 161 |
| abstract_inverted_index.features | 193 |
| abstract_inverted_index.improved | 88 |
| abstract_inverted_index.indicate | 199 |
| abstract_inverted_index.network, | 72 |
| abstract_inverted_index.omission | 118 |
| abstract_inverted_index.presence | 58 |
| abstract_inverted_index.present, | 19 |
| abstract_inverted_index.training | 108 |
| abstract_inverted_index.DeepGlobe | 216 |
| abstract_inverted_index.attention | 90 |
| abstract_inverted_index.combining | 192 |
| abstract_inverted_index.datasets, | 220, 241 |
| abstract_inverted_index.datasets. | 30, 129 |
| abstract_inverted_index.different | 173, 195 |
| abstract_inverted_index.elements. | 62 |
| abstract_inverted_index.emergency | 16 |
| abstract_inverted_index.extremely | 140, 251 |
| abstract_inverted_index.mechanism | 91 |
| abstract_inverted_index.planning, | 12 |
| abstract_inverted_index.response. | 17 |
| abstract_inverted_index.Extraction | 78, 154 |
| abstract_inverted_index.Meanwhile, | 228 |
| abstract_inverted_index.capability | 137 |
| abstract_inverted_index.continuity | 33 |
| abstract_inverted_index.coordinate | 89 |
| abstract_inverted_index.extracting | 249 |
| abstract_inverted_index.extraction | 5, 39, 122, 136 |
| abstract_inverted_index.integrates | 82 |
| abstract_inverted_index.introduces | 69, 145 |
| abstract_inverted_index.relatively | 46 |
| abstract_inverted_index.unlabeled, | 139, 250 |
| abstract_inverted_index.(MSIF-Net), | 156 |
| abstract_inverted_index.(SWGE-Net), | 80 |
| abstract_inverted_index.Information | 151 |
| abstract_inverted_index.Multi-scale | 150 |
| abstract_inverted_index.effectively | 93 |
| abstract_inverted_index.improvement | 223 |
| abstract_inverted_index.information | 100, 171 |
| abstract_inverted_index.interfering | 61 |
| abstract_inverted_index.navigation, | 14 |
| abstract_inverted_index.outperforms | 202 |
| abstract_inverted_index.performance | 247 |
| abstract_inverted_index.qualitative | 255 |
| abstract_inverted_index.recognizing | 190 |
| abstract_inverted_index.Experimental | 197 |
| abstract_inverted_index.Furthermore, | 104 |
| abstract_inverted_index.completeness | 36 |
| abstract_inverted_index.experiments. | 256 |
| abstract_inverted_index.respectively | 221 |
| abstract_inverted_index.Self-weighted | 74 |
| abstract_inverted_index.distribution, | 55 |
| abstract_inverted_index.pooling-based | 183 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.74778269 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |