Machine learning and SLIC for Tree Canopies segmentation in urban areas
This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better res...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Correa Martins, José Augusto [verfasserIn] Menezes, Geazy [verfasserIn] Gonçalves, Wesley [verfasserIn] Sant’Ana, Diego André [verfasserIn] Osco, Lucas Prado [verfasserIn] Liesenberg, Veraldo [verfasserIn] Li, Jonathan [verfasserIn] Ma, Lingfei [verfasserIn] Oliveira, Paulo Tarso [verfasserIn] Astolfi, Gilberto [verfasserIn] Pistori, Hemerson [verfasserIn] Junior, José Marcato [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Ecological informatics - Amsterdam [u.a.] : Elsevier, 2006, 66 |
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| Übergeordnetes Werk: |
volume:66 |
| DOI / URN: |
10.1016/j.ecoinf.2021.101465 |
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| Katalog-ID: |
ELV007054157 |
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| 100 | 1 | |a Correa Martins, José Augusto |e verfasserin |0 (orcid)0000-0003-0668-8224 |4 aut | |
| 245 | 1 | 0 | |a Machine learning and SLIC for Tree Canopies segmentation in urban areas |
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| 520 | |a This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from high-resolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications. | ||
| 650 | 4 | |a Urban environment | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Remote sensing | |
| 650 | 4 | |a Photogrammetry | |
| 650 | 4 | |a Computer vision | |
| 700 | 1 | |a Menezes, Geazy |e verfasserin |4 aut | |
| 700 | 1 | |a Gonçalves, Wesley |e verfasserin |4 aut | |
| 700 | 1 | |a Sant’Ana, Diego André |e verfasserin |0 (orcid)0000-0002-9209-9129 |4 aut | |
| 700 | 1 | |a Osco, Lucas Prado |e verfasserin |4 aut | |
| 700 | 1 | |a Liesenberg, Veraldo |e verfasserin |0 (orcid)0000-0003-0564-7818 |4 aut | |
| 700 | 1 | |a Li, Jonathan |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Lingfei |e verfasserin |0 (orcid)0000-0001-8893-9693 |4 aut | |
| 700 | 1 | |a Oliveira, Paulo Tarso |e verfasserin |4 aut | |
| 700 | 1 | |a Astolfi, Gilberto |e verfasserin |4 aut | |
| 700 | 1 | |a Pistori, Hemerson |e verfasserin |0 (orcid)0000-0001-8181-760X |4 aut | |
| 700 | 1 | |a Junior, José Marcato |e verfasserin |4 aut | |
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10.1016/j.ecoinf.2021.101465 doi (DE-627)ELV007054157 (ELSEVIER)S1574-9541(21)00256-9 DE-627 ger DE-627 rda eng 610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl Correa Martins, José Augusto verfasserin (orcid)0000-0003-0668-8224 aut Machine learning and SLIC for Tree Canopies segmentation in urban areas 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from high-resolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications. Urban environment Machine learning Remote sensing Photogrammetry Computer vision Menezes, Geazy verfasserin aut Gonçalves, Wesley verfasserin aut Sant’Ana, Diego André verfasserin (orcid)0000-0002-9209-9129 aut Osco, Lucas Prado verfasserin aut Liesenberg, Veraldo verfasserin (orcid)0000-0003-0564-7818 aut Li, Jonathan verfasserin aut Ma, Lingfei verfasserin (orcid)0000-0001-8893-9693 aut Oliveira, Paulo Tarso verfasserin aut Astolfi, Gilberto verfasserin aut Pistori, Hemerson verfasserin (orcid)0000-0001-8181-760X aut Junior, José Marcato verfasserin aut Enthalten in Ecological informatics Amsterdam [u.a.] : Elsevier, 2006 66 Online-Ressource (DE-627)506285960 (DE-600)2218079-5 (DE-576)25927349X 1878-0512 nnns volume:66 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 Ökologie: Allgemeines 42.11 Biomathematik Biokybernetik AR 66 |
| spelling |
10.1016/j.ecoinf.2021.101465 doi (DE-627)ELV007054157 (ELSEVIER)S1574-9541(21)00256-9 DE-627 ger DE-627 rda eng 610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl Correa Martins, José Augusto verfasserin (orcid)0000-0003-0668-8224 aut Machine learning and SLIC for Tree Canopies segmentation in urban areas 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from high-resolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications. Urban environment Machine learning Remote sensing Photogrammetry Computer vision Menezes, Geazy verfasserin aut Gonçalves, Wesley verfasserin aut Sant’Ana, Diego André verfasserin (orcid)0000-0002-9209-9129 aut Osco, Lucas Prado verfasserin aut Liesenberg, Veraldo verfasserin (orcid)0000-0003-0564-7818 aut Li, Jonathan verfasserin aut Ma, Lingfei verfasserin (orcid)0000-0001-8893-9693 aut Oliveira, Paulo Tarso verfasserin aut Astolfi, Gilberto verfasserin aut Pistori, Hemerson verfasserin (orcid)0000-0001-8181-760X aut Junior, José Marcato verfasserin aut Enthalten in Ecological informatics Amsterdam [u.a.] : Elsevier, 2006 66 Online-Ressource (DE-627)506285960 (DE-600)2218079-5 (DE-576)25927349X 1878-0512 nnns volume:66 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 Ökologie: Allgemeines 42.11 Biomathematik Biokybernetik AR 66 |
| allfields_unstemmed |
10.1016/j.ecoinf.2021.101465 doi (DE-627)ELV007054157 (ELSEVIER)S1574-9541(21)00256-9 DE-627 ger DE-627 rda eng 610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl Correa Martins, José Augusto verfasserin (orcid)0000-0003-0668-8224 aut Machine learning and SLIC for Tree Canopies segmentation in urban areas 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from high-resolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications. Urban environment Machine learning Remote sensing Photogrammetry Computer vision Menezes, Geazy verfasserin aut Gonçalves, Wesley verfasserin aut Sant’Ana, Diego André verfasserin (orcid)0000-0002-9209-9129 aut Osco, Lucas Prado verfasserin aut Liesenberg, Veraldo verfasserin (orcid)0000-0003-0564-7818 aut Li, Jonathan verfasserin aut Ma, Lingfei verfasserin (orcid)0000-0001-8893-9693 aut Oliveira, Paulo Tarso verfasserin aut Astolfi, Gilberto verfasserin aut Pistori, Hemerson verfasserin (orcid)0000-0001-8181-760X aut Junior, José Marcato verfasserin aut Enthalten in Ecological informatics Amsterdam [u.a.] : Elsevier, 2006 66 Online-Ressource (DE-627)506285960 (DE-600)2218079-5 (DE-576)25927349X 1878-0512 nnns volume:66 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 Ökologie: Allgemeines 42.11 Biomathematik Biokybernetik AR 66 |
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10.1016/j.ecoinf.2021.101465 doi (DE-627)ELV007054157 (ELSEVIER)S1574-9541(21)00256-9 DE-627 ger DE-627 rda eng 610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl Correa Martins, José Augusto verfasserin (orcid)0000-0003-0668-8224 aut Machine learning and SLIC for Tree Canopies segmentation in urban areas 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from high-resolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications. Urban environment Machine learning Remote sensing Photogrammetry Computer vision Menezes, Geazy verfasserin aut Gonçalves, Wesley verfasserin aut Sant’Ana, Diego André verfasserin (orcid)0000-0002-9209-9129 aut Osco, Lucas Prado verfasserin aut Liesenberg, Veraldo verfasserin (orcid)0000-0003-0564-7818 aut Li, Jonathan verfasserin aut Ma, Lingfei verfasserin (orcid)0000-0001-8893-9693 aut Oliveira, Paulo Tarso verfasserin aut Astolfi, Gilberto verfasserin aut Pistori, Hemerson verfasserin (orcid)0000-0001-8181-760X aut Junior, José Marcato verfasserin aut Enthalten in Ecological informatics Amsterdam [u.a.] : Elsevier, 2006 66 Online-Ressource (DE-627)506285960 (DE-600)2218079-5 (DE-576)25927349X 1878-0512 nnns volume:66 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 Ökologie: Allgemeines 42.11 Biomathematik Biokybernetik AR 66 |
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10.1016/j.ecoinf.2021.101465 doi (DE-627)ELV007054157 (ELSEVIER)S1574-9541(21)00256-9 DE-627 ger DE-627 rda eng 610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl Correa Martins, José Augusto verfasserin (orcid)0000-0003-0668-8224 aut Machine learning and SLIC for Tree Canopies segmentation in urban areas 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from high-resolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications. Urban environment Machine learning Remote sensing Photogrammetry Computer vision Menezes, Geazy verfasserin aut Gonçalves, Wesley verfasserin aut Sant’Ana, Diego André verfasserin (orcid)0000-0002-9209-9129 aut Osco, Lucas Prado verfasserin aut Liesenberg, Veraldo verfasserin (orcid)0000-0003-0564-7818 aut Li, Jonathan verfasserin aut Ma, Lingfei verfasserin (orcid)0000-0001-8893-9693 aut Oliveira, Paulo Tarso verfasserin aut Astolfi, Gilberto verfasserin aut Pistori, Hemerson verfasserin (orcid)0000-0001-8181-760X aut Junior, José Marcato verfasserin aut Enthalten in Ecological informatics Amsterdam [u.a.] : Elsevier, 2006 66 Online-Ressource (DE-627)506285960 (DE-600)2218079-5 (DE-576)25927349X 1878-0512 nnns volume:66 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 Ökologie: Allgemeines 42.11 Biomathematik Biokybernetik AR 66 |
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Enthalten in Ecological informatics 66 volume:66 |
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Enthalten in Ecological informatics 66 volume:66 |
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Ökologie: Allgemeines Biomathematik Biokybernetik |
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Urban environment Machine learning Remote sensing Photogrammetry Computer vision |
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Ecological informatics |
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Correa Martins, José Augusto @@aut@@ Menezes, Geazy @@aut@@ Gonçalves, Wesley @@aut@@ Sant’Ana, Diego André @@aut@@ Osco, Lucas Prado @@aut@@ Liesenberg, Veraldo @@aut@@ Li, Jonathan @@aut@@ Ma, Lingfei @@aut@@ Oliveira, Paulo Tarso @@aut@@ Astolfi, Gilberto @@aut@@ Pistori, Hemerson @@aut@@ Junior, José Marcato @@aut@@ |
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2021-01-01T00:00:00Z |
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Correa Martins, José Augusto |
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610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl Machine learning and SLIC for Tree Canopies segmentation in urban areas Urban environment Machine learning Remote sensing Photogrammetry Computer vision |
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Correa Martins, José Augusto Menezes, Geazy Gonçalves, Wesley Sant’Ana, Diego André Osco, Lucas Prado Liesenberg, Veraldo Li, Jonathan Ma, Lingfei Oliveira, Paulo Tarso Astolfi, Gilberto Pistori, Hemerson Junior, José Marcato |
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machine learning and slic for tree canopies segmentation in urban areas |
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Machine learning and SLIC for Tree Canopies segmentation in urban areas |
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This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from high-resolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications. |
| abstractGer |
This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from high-resolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications. |
| abstract_unstemmed |
This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from high-resolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications. |
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Machine learning and SLIC for Tree Canopies segmentation in urban areas |
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Menezes, Geazy Gonçalves, Wesley Sant’Ana, Diego André Osco, Lucas Prado Liesenberg, Veraldo Li, Jonathan Ma, Lingfei Oliveira, Paulo Tarso Astolfi, Gilberto Pistori, Hemerson Junior, José Marcato |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV007054157</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524150924.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230506s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ecoinf.2021.101465</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV007054157</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1574-9541(21)00256-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="a">333.7</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.90</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.11</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Correa Martins, José Augusto</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-0668-8224</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine learning and SLIC for Tree Canopies segmentation in urban areas</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from high-resolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Urban environment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Remote sensing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Photogrammetry</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer vision</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Menezes, Geazy</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gonçalves, Wesley</subfield><subfield code="e">verfasserin</subfield><subfield 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