Mask R-CNN for Geospatial Object Detection

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Author(s)

Dalal AL-Alimi 1,2,* Yuxiang Shao 2 Ahamed Alalimi 3 Ahmed Abdu 4

1. Faculty of Engineering, Sana’a University, Sana’a, Yemen

2. School of Computer Science, China University of Geosciences, Wuhan, China

3. Faculty of Oil and Natural Gas from China University of Geosciences, Wuhan, China

4. Faculty of Information Engineering, China University of Geoscience, Wuhan, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2020.05.05

Received: 2 Dec. 2019 / Revised: 20 Dec. 2019 / Accepted: 12 Dec. 2019 / Published: 8 Oct. 2020

Index Terms

Mask R-CNN, Faster R-CNN, RoIAlign, object detection, instance segmentation

Abstract

Geospatial imaging technique has opened a door for researchers to implement multiple beneficial applications in many fields, including military investigation, disaster relief, and urban traffic control. As the resolution of geospatial images has increased in recent years, the detection of geospatial objects has attracted a lot of researchers. Mask R-CNN had been designed to identify an object outlines at the pixel level (instance segmentation), and for object detection in natural images. This study describes the Mask R-CNN model and uses it to detect objects in geospatial images. This experiment was prepared an existing dataset to be suitable with object segmentation, and it shows that Mask R-CNN also has the ability to be used in geospatial object detection and it introduces good results to extract the ten classes dataset of Seg-VHR-10.

Cite This Paper

Dalal AL-Alimi, Yuxiang Shao, Ahamed Alalimi, Ahmed Abdu, "Mask R-CNN for Geospatial Object Detection", International Journal of Information Technology and Computer Science(IJITCS), Vol.12, No.5, pp.63-72, 2020. DOI:10.5815/ijitcs.2020.05.05

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