Semi-Automatic Road Network Extraction From Digital Images Using Object-Based Classification and Morphological Operators

Nunes, Darlan Miranda; Medeiros, Nilcilene das Graças; Santos, Afonso de Paula dos

doi:10.1590/s1982-21702018000400030

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…However, they all need a sufficient number of representative training data and the prediction ability is highly related to the training samples fed into the model [3,19,20]. Owing to the complexity of roads themselves, automatic road extraction models cannot achieve good results through the direct application on another dataset [21][22][23][24]. Therefore, the limited remote-sensing datasets with labels are an obstacle to developing and evaluating new deep learning methods [19,25].…”

Section: Introductionmentioning

confidence: 99%

“…In accordance with the process and focus of the extraction algorithm, existing semiautomatic road extraction methods are mainly based on regional growth [28][29][30]; dynamic programming [31][32][33]; edge detection [34], including contour identification by finding the gradient and potential of the image [35] followed by edge thinning and division [36]; image segmentation [9,10,23]; template matching [37,38]; active contour models such as Snake [39][40][41]; and machine learning and neural networks [2,21,37,42]. However, low efficiency and poor robustness remain as problems.…”

Section: Introductionmentioning

confidence: 99%

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Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images

et al. 2022

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Road network extraction plays a critical role in data updating, urban development, and decision support. To improve the efficiency of labeling road datasets and addressing the problems of traditional methods of manually extracting road networks from high-resolution images, such as their slow speed and heavy workload, this paper proposes a semi-automatic method of road network extraction from high-resolution remote-sensing images. The proposed method needs only a few points to extract a single road in the image. After the roads are extracted one by one, the road network is generated according to the width of each road and the spatial relationships among the roads. For this purpose, we use regional growth, morphology, vector tracking, vector simplification, endpoint modification, road connections, and intersection connections to generate road networks. Experiments on four images with different terrains and different resolutions show that this method has high extraction accuracy under different image conditions. The comparisons with the semi-automatic GVF-snake method based on regional growth also showed its advantages and potentiality. The proposed method is a novel form of semi-automatic road network extraction, and it significantly increases the efficiency of road network extraction.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images

et al. 2022

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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%

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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Semi-Automatic Road Network Extraction From Digital Images Using Object-Based Classification and Morphological Operators

Cited by 3 publications

References 16 publications

Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images

Semi-Automatic Method of Extracting Road Networks from High-Resolution 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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