Deep convolutional neural network (DCNN) for road network extraction from fusion of airborne laser scanning (ALS) data and highest resolution image in the urban environment

• Grant number: 21/03586-2
• Support Opportunities: Regular Research Grants
• Start date: August 01, 2021
• End date: January 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Geosciences
• Principal Investigator: Aluir Porfírio Dal Poz
• Grantee: Aluir Porfírio Dal Poz
• Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil

Abstract

Brazil is a big country that has a challenging extension area and millions of kilometers of roads distributed irregularly. The road information is crucial for government planners and the economy (e.g., logistics). Since the 70s, several authors have created methods for automatic road extraction. All have advantages and disadvantages. However, the flexibility requirements with big data and handcraft processes affect the accuracy and quickness of extraction. After 2000 years, new machine learning approaches arise in many fields of knowledge. Among them is Remote Sensing. These tools permit to extract information of extensive data from multiple sources. Since the year 2014, deep neural convolutional networks have obtained exciting results in the Remote Sensing field, mainly due to high accuracy, automatic feature extraction, and flexibility. LASER Detection and Ranging (LiDAR) is a data source that could supply more information than only visible spectral range. The research aims to create a methodology of automatic road extraction in the urban environment using data fusion LiDAR-Orthoimages through the neural convolutional network (U-Net architecture). The methodology was split into two steps to analyze the data source contribution to road extraction, mainly in occlusion and obstruction cases. Lastly, this research uses deep learning as a tool for automatic information extraction in Remote Sensing that, nowadays, is state of the art. So, the methodology purposes road extraction quickly with quality control. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DAL POZI, A. P.; MARTINS, E. F. O.; ZANIN, R. B.; ZIATANOVA, S; SITHOLE, G; BARTON, J. ROAD NETWORK EXTRACTION USING GPS TRAJECTORIES BASED ON MORPHOLOGICAL AND SKELETONIZATION ALGORITHMS. XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III, v. 43-B4, p. 7-pg., 2022-01-01. (21/03586-2)

FILHO ANTONIO; SHIMABUKURO, MILTON; POZ, ALUIR DAL. Deep Learning Multimodal Fusion for Road Network Extraction: Context and Contour Improvement. IEEE Geoscience and Remote Sensing Letters, v. 20, p. 5-pg., 2023-01-01. (21/03586-2)