Semiautomatic Road Extraction From VHR Images Based on Multiscale and Spectral Angle in Case of Earthquake

Coulibaly, Idrissa; Spiric, Nicolas; Lepage, Richard; St-Jacques, Michèle

doi:10.1109/jstars.2017.2760282

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…Road extraction from remote sensing images usually contains two subtasks: road area extraction and road centerline extraction [28,29]. Road area extraction methods produce pixellevel labeling of roads [1,4,[29][30][31][32][33][34][35][36][37][38], while skeletons of roads are extracted for road centerline extraction [8,27,28,[39][40][41][42][43][44][45][46].…”

Section: B Road Extraction From Remote Sensing Imagesmentioning

confidence: 99%

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Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images

Chen

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Automatic road extraction from remote sensing images plays an important role for navigation, intelligent transportation and road network update etc. Convolutional Neural Network (CNN) based methods have presented many achievements for road extraction from remote sensing images. CNN based methods require a large dataset with high quality labels for model training. However, there is still few standard and large dataset which is specially designed for road extraction from optical remote sensing images. Besides, the existing end-to-end CNN models for road extraction from remote sensing images are usually with symmetric structure, studying on asymmetric structure between encoding and decoding is rare. To address the above problems, this paper first provides a publicly available dataset LRSNY for road extraction from optical remote sensing images with manually labelled labels. Second, we propose a reconstruction bias U-Net for road extraction from remote sensing images. In our model, we increase the decoding branches to obtain multiple semantic information from different up-samplings. Experimental results show that our method achieves better performance compared with other six state-of-the-art segmentation models when testing on our LRSNY dataset. We also test on Massachusetts and Shaoshan datasets. The good performances on the two datasets further prove the effectiveness of our method.

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Section: B Road Extraction From Remote Sensing Imagesmentioning

confidence: 99%

“…values about road extraction from remote sensing images, much research has focused on automatic road extraction from remote sensing images [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images

Chen

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Machine learning algorithms are designed to enhance performance by effectively teaching the computer how to extract the desired spatial data from imagery with both precision and accuracy. AFE has been leveraged for a myriad of purposes, such as mapping agricultural land use [ 13 – 16 ] and water boundaries [ 17 , 18 ], estimating human and livestock populations [ 19 , 20 ], road feature extraction [ 21 , 22 ], building feature extraction [ 23 – 29 ], and to support disaster relief efforts [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning

et al. 2021

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Background Social instability and logistical factors like the displacement of vulnerable populations, the difficulty of accessing these populations, and the lack of geographic information for hard-to-reach areas continue to serve as barriers to global essential immunizations (EI). Microplanning, a population-based, healthcare intervention planning method has begun to leverage geographic information system (GIS) technology and geospatial methods to improve the remote identification and mapping of vulnerable populations to ensure inclusion in outreach and immunization services, when feasible. We compare two methods of accomplishing a remote inventory of building locations to assess their accuracy and similarity to currently employed microplan line-lists in the study area. Methods The outputs of a crowd-sourced digitization effort, or mapathon, were compared to those of a machine-learning algorithm for digitization, referred to as automatic feature extraction (AFE). The following accuracy assessments were employed to determine the performance of each feature generation method: (1) an agreement analysis of the two methods assessed the occurrence of matches across the two outputs, where agreements were labeled as “befriended” and disagreements as “lonely”; (2) true and false positive percentages of each method were calculated in comparison to satellite imagery; (3) counts of features generated from both the mapathon and AFE were statistically compared to the number of features listed in the microplan line-list for the study area; and (4) population estimates for both feature generation method were determined for every structure identified assuming a total of three households per compound, with each household averaging two adults and 5 children. Results The mapathon and AFE outputs detected 92,713 and 53,150 features, respectively. A higher proportion (30%) of AFE features were befriended compared with befriended mapathon points (28%). The AFE had a higher true positive rate (90.5%) of identifying structures than the mapathon (84.5%). The difference in the average number of features identified per area between the microplan and mapathon points was larger (t = 3.56) than the microplan and AFE (t = − 2.09) (alpha = 0.05). Conclusions Our findings indicate AFE outputs had higher agreement (i.e., befriended), slightly higher likelihood of correctly identifying a structure, and were more similar to the local microplan line-lists than the mapathon outputs. These findings suggest AFE may be more accurate for identifying structures in high-resolution satellite imagery than mapathons. However, they both had their advantages and the ideal method would utilize both methods in tandem.

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“…Machine learning algorithms are designed to enhance performance by effectively teaching the computer how to extract the desired spatial data from imagery with both precision and accuracy. AFE has been leveraged for a myriad of purposes, such as mapping agricultural land use (13)(14)(15)(16) and water boundaries (17,18), estimating human and livestock populations (19,20), road feature extraction (21,22), building feature extraction (23)(24)(25)(26)(27)(28)(29), and to support disaster relief efforts (30,31).…”

Section: Introductionmentioning

confidence: 99%

“…Like mapathons, AFE relies on high-resolution imagery for optimal performance, but image collection parameters can be re ned to account for cloud cover, thick vegetation, and low spectral resolution. Additionally, using a time-series of images can improve the accuracy of feature detection by minimizing false-positives (14,18) and are especially helpful when analyzing pre-and post-disaster impacts to roads (30) and facilities (31). AFE could be particularly useful for essential immunization efforts because it generates spatial data from imagery rapidly and has the potential to be more accurate than mapathons.…”

Section: Introductionmentioning

confidence: 99%

Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning

Mendes

Palmer

Berens

et al. 2021

Preprint

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Background The barriers to global essential immunization (EI) coverage are increasingly due to social instability and logistical factors like the displacement of vulnerable populations, the difficulty of accessing these populations, and the lack of geographic information for hard-to-reach areas. Microplanning, a population-based, healthcare intervention planning method has begun to leverage geographic information system (GIS) technology and geospatial methods to improve the remote identification and mapping of vulnerable populations to ensure inclusion in outreach and immunization services, when feasible. We compare two methods of accomplishing a remote inventory of building locations to assess their accuracy and similarity to currently employed microplan line-lists in the study area. Methods The outputs of a crowd-sourced digitization effort, or mapathon, were compared to those of a machine-learning algorithm for digitization, referred to as automatic feature extraction (AFE). The accuracy assessments were conducted: 1) an agreement analysis of the two methods assessed the occurrence of matches across the two outputs, where agreements were labeled as “befriended” and disagreements as “lonely”; 2) true and false positive percentages of each method were calculated in comparison to satellite imagery; and 3) counts of features generated from both the mapathon and AFE were statistically compared to the number of features listed in the microplan line-list for the study area. Results The mapathon and AFE outputs detected 92,713 and 53,150 features, respectively. A higher proportion (30%) of AFE features were befriended compared with befriended mapathon points (28%). The AFE had a higher true positive rate (90.5%) of identifying structures than the mapathon (84.5%). The difference in the average number of features identified per area between the microplan and mapathon points was larger (t = 3.56) than the microplan and AFE (t = -2.09) (alpha = 0.05). Conclusions Our findings indicate AFE outputs had higher agreement (i.e., befriended), slightly higher likelihood of correctly identifying a structure, and were more similar to the local microplan line-lists than the mapathon outputs. These findings suggest AFE may be more accurate for identifying structures in high-resolution satellite imagery than mapathons. However, they both had their advantages and the ideal method would utilize both methods in tandem.

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Semiautomatic Road Extraction From VHR Images Based on Multiscale and Spectral Angle in Case of Earthquake

Cited by 16 publications

References 15 publications

Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images

Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images

Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning

Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning

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