Accurate Rural Road Network With Improved Dataset for Strengthen Wildfire Emergency Response Article Swipe
Miguel Lourenço
,
Duarte O. Caetano
,
André Mora
,
Luís B. Oliveira
,
Henrique Oliveira
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3600192
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3600192
Wildfires pose significant threats to ecosystems worldwide, making rapid firefighting responses essential. Firefighters face major challenges in advanced-stage fires, highlighting the need for early detection and improved operational support. However, current rural road mapping methods often fail under occlusions from vegetation and shadows, lack adaptability to wildfire-prone regions, and offer limited automation for large-scale applications. This study introduces a system that integrates U-Net-based deep learning architectures with post-processing to detect rural roads from aerial imagery. A manually annotated dataset of 1,044 orthorectified aerial images from wildfire-prone areas was augmented to 4,700 samples to improve segmentation robustness under occlusion. The system follows a two-step process: rural road segmentation and post-processing to remove small connected components and reconnect occluded road segments. Four architectures: VGG16 U-Net, VGG19 U-Net, ResNet50 U-Net, and MobileNet U-Net were tested with extensive hyperparameter tuning, both before and after augmentation. Data augmentation significantly improved performance, with post-processing providing modest yet consistent gains. The best results reached 85.1% Precision, 81.9% Recall, and a 71.7% Jaccard index. MobileNet U-Net showed the largest post-processing improvement. Overall, the approach enables automated, accurate rural road mapping to support operational wildfire response, offering a practical and adaptable tool for crisis management.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3600192
- OA Status
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- References
- 47
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All OpenAlex metadata
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https://openalex.org/W4413318868Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/access.2025.3600192Digital Object Identifier
- Title
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Accurate Rural Road Network With Improved Dataset for Strengthen Wildfire Emergency ResponseWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Miguel Lourenço, Duarte O. Caetano, André Mora, Luís B. Oliveira, Henrique OliveiraList of authors in order
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https://doi.org/10.1109/access.2025.3600192Publisher 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/10.1109/access.2025.3600192Direct OA link when available
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Emergency response, Disaster response, Computer science, Emergency management, Remote sensing, Medical emergency, Geography, Medicine, Political science, LawTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4313518774, https://openalex.org/W2921476973, https://openalex.org/W3017131514, https://openalex.org/W3040231701, https://openalex.org/W2176950688, https://openalex.org/W2794284562, https://openalex.org/W2807567209, https://openalex.org/W3140854437, https://openalex.org/W2593886839, https://openalex.org/W2809254203, https://openalex.org/W3093596417, https://openalex.org/W2997604048, https://openalex.org/W2883837659, https://openalex.org/W110283499, https://openalex.org/W3013738543, https://openalex.org/W3047433257, https://openalex.org/W2908020915, https://openalex.org/W2809598685, https://openalex.org/W4380080118, https://openalex.org/W1901129140, https://openalex.org/W3123982987, https://openalex.org/W4389961031, https://openalex.org/W4220849090, https://openalex.org/W4288722736, https://openalex.org/W2967073193, https://openalex.org/W2945957599, https://openalex.org/W4213058019, https://openalex.org/W1969483458, https://openalex.org/W2995090317, https://openalex.org/W2901550124, https://openalex.org/W2963881378, https://openalex.org/W2780861787, https://openalex.org/W2194775991, https://openalex.org/W2969707169, https://openalex.org/W3195217426, https://openalex.org/W2964255128, https://openalex.org/W2048038513, https://openalex.org/W2167215479, https://openalex.org/W2141201928, https://openalex.org/W2100037535, https://openalex.org/W2011301426, https://openalex.org/W2997591727, https://openalex.org/W2897722020, https://openalex.org/W3021970402, https://openalex.org/W4399206928, https://openalex.org/W4385232265, https://openalex.org/W4399241708 |
| referenced_works_count | 47 |
| abstract_inverted_index.A | 75 |
| abstract_inverted_index.a | 58, 101, 162, 188 |
| abstract_inverted_index.in | 16 |
| abstract_inverted_index.of | 79 |
| abstract_inverted_index.to | 4, 45, 68, 89, 92, 109, 182 |
| abstract_inverted_index.The | 98, 153 |
| abstract_inverted_index.and | 25, 41, 48, 107, 114, 127, 138, 161, 190 |
| abstract_inverted_index.for | 22, 52, 193 |
| abstract_inverted_index.the | 20, 169, 174 |
| abstract_inverted_index.was | 87 |
| abstract_inverted_index.yet | 150 |
| abstract_inverted_index.Data | 141 |
| abstract_inverted_index.Four | 119 |
| abstract_inverted_index.This | 55 |
| abstract_inverted_index.best | 154 |
| abstract_inverted_index.both | 136 |
| abstract_inverted_index.deep | 63 |
| abstract_inverted_index.face | 13 |
| abstract_inverted_index.fail | 36 |
| abstract_inverted_index.from | 39, 72, 84 |
| abstract_inverted_index.lack | 43 |
| abstract_inverted_index.need | 21 |
| abstract_inverted_index.pose | 1 |
| abstract_inverted_index.road | 32, 105, 117, 180 |
| abstract_inverted_index.that | 60 |
| abstract_inverted_index.tool | 192 |
| abstract_inverted_index.were | 130 |
| abstract_inverted_index.with | 66, 132, 146 |
| abstract_inverted_index.1,044 | 80 |
| abstract_inverted_index.4,700 | 90 |
| abstract_inverted_index.71.7% | 163 |
| abstract_inverted_index.81.9% | 159 |
| abstract_inverted_index.85.1% | 157 |
| abstract_inverted_index.U-Net | 129, 167 |
| abstract_inverted_index.VGG16 | 121 |
| abstract_inverted_index.VGG19 | 123 |
| abstract_inverted_index.after | 139 |
| abstract_inverted_index.areas | 86 |
| abstract_inverted_index.early | 23 |
| abstract_inverted_index.major | 14 |
| abstract_inverted_index.offer | 49 |
| abstract_inverted_index.often | 35 |
| abstract_inverted_index.rapid | 8 |
| abstract_inverted_index.roads | 71 |
| abstract_inverted_index.rural | 31, 70, 104, 179 |
| abstract_inverted_index.small | 111 |
| abstract_inverted_index.study | 56 |
| abstract_inverted_index.under | 37, 96 |
| abstract_inverted_index.U-Net, | 122, 124, 126 |
| abstract_inverted_index.aerial | 73, 82 |
| abstract_inverted_index.before | 137 |
| abstract_inverted_index.crisis | 194 |
| abstract_inverted_index.detect | 69 |
| abstract_inverted_index.fires, | 18 |
| abstract_inverted_index.gains. | 152 |
| abstract_inverted_index.images | 83 |
| abstract_inverted_index.index. | 165 |
| abstract_inverted_index.making | 7 |
| abstract_inverted_index.modest | 149 |
| abstract_inverted_index.remove | 110 |
| abstract_inverted_index.showed | 168 |
| abstract_inverted_index.system | 59, 99 |
| abstract_inverted_index.tested | 131 |
| abstract_inverted_index.Jaccard | 164 |
| abstract_inverted_index.Recall, | 160 |
| abstract_inverted_index.current | 30 |
| abstract_inverted_index.dataset | 78 |
| abstract_inverted_index.enables | 176 |
| abstract_inverted_index.follows | 100 |
| abstract_inverted_index.improve | 93 |
| abstract_inverted_index.largest | 170 |
| abstract_inverted_index.limited | 50 |
| abstract_inverted_index.mapping | 33, 181 |
| abstract_inverted_index.methods | 34 |
| abstract_inverted_index.reached | 156 |
| abstract_inverted_index.results | 155 |
| abstract_inverted_index.samples | 91 |
| abstract_inverted_index.support | 183 |
| abstract_inverted_index.threats | 3 |
| abstract_inverted_index.tuning, | 135 |
| abstract_inverted_index.However, | 29 |
| abstract_inverted_index.Overall, | 173 |
| abstract_inverted_index.ResNet50 | 125 |
| abstract_inverted_index.accurate | 178 |
| abstract_inverted_index.approach | 175 |
| abstract_inverted_index.imagery. | 74 |
| abstract_inverted_index.improved | 26, 144 |
| abstract_inverted_index.learning | 64 |
| abstract_inverted_index.manually | 76 |
| abstract_inverted_index.occluded | 116 |
| abstract_inverted_index.offering | 187 |
| abstract_inverted_index.process: | 103 |
| abstract_inverted_index.regions, | 47 |
| abstract_inverted_index.shadows, | 42 |
| abstract_inverted_index.support. | 28 |
| abstract_inverted_index.two-step | 102 |
| abstract_inverted_index.wildfire | 185 |
| abstract_inverted_index.MobileNet | 128, 166 |
| abstract_inverted_index.Wildfires | 0 |
| abstract_inverted_index.adaptable | 191 |
| abstract_inverted_index.annotated | 77 |
| abstract_inverted_index.augmented | 88 |
| abstract_inverted_index.connected | 112 |
| abstract_inverted_index.detection | 24 |
| abstract_inverted_index.extensive | 133 |
| abstract_inverted_index.practical | 189 |
| abstract_inverted_index.providing | 148 |
| abstract_inverted_index.reconnect | 115 |
| abstract_inverted_index.response, | 186 |
| abstract_inverted_index.responses | 10 |
| abstract_inverted_index.segments. | 118 |
| abstract_inverted_index.Precision, | 158 |
| abstract_inverted_index.automated, | 177 |
| abstract_inverted_index.automation | 51 |
| abstract_inverted_index.challenges | 15 |
| abstract_inverted_index.components | 113 |
| abstract_inverted_index.consistent | 151 |
| abstract_inverted_index.ecosystems | 5 |
| abstract_inverted_index.essential. | 11 |
| abstract_inverted_index.integrates | 61 |
| abstract_inverted_index.introduces | 57 |
| abstract_inverted_index.occlusion. | 97 |
| abstract_inverted_index.occlusions | 38 |
| abstract_inverted_index.robustness | 95 |
| abstract_inverted_index.vegetation | 40 |
| abstract_inverted_index.worldwide, | 6 |
| abstract_inverted_index.U-Net-based | 62 |
| abstract_inverted_index.large-scale | 53 |
| abstract_inverted_index.management. | 195 |
| abstract_inverted_index.operational | 27, 184 |
| abstract_inverted_index.significant | 2 |
| abstract_inverted_index.Firefighters | 12 |
| abstract_inverted_index.adaptability | 44 |
| abstract_inverted_index.augmentation | 142 |
| abstract_inverted_index.firefighting | 9 |
| abstract_inverted_index.highlighting | 19 |
| abstract_inverted_index.improvement. | 172 |
| abstract_inverted_index.performance, | 145 |
| abstract_inverted_index.segmentation | 94, 106 |
| abstract_inverted_index.applications. | 54 |
| abstract_inverted_index.architectures | 65 |
| abstract_inverted_index.augmentation. | 140 |
| abstract_inverted_index.significantly | 143 |
| abstract_inverted_index.advanced-stage | 17 |
| abstract_inverted_index.architectures: | 120 |
| abstract_inverted_index.hyperparameter | 134 |
| abstract_inverted_index.orthorectified | 81 |
| abstract_inverted_index.wildfire-prone | 46, 85 |
| abstract_inverted_index.post-processing | 67, 108, 147, 171 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.38053559 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |