Methods in the spatial deep learning: current status and future direction

• Published in: Spatial information research (Online) Vol. 30; no. 2; pp. 215 - 232
• Main Authors: Mishra, Bhogendra, Dahal, Ashok, Luintel, Nirajan, Shahi, Tej Bahadur, Panthi, Saroj, Pariyar, Shiva, Ghimire, Bhoj Raj
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.04.2022; 대한공간정보학회
• Subjects: Database Management; Earth and Environmental Science; Geographical Information Systems/Cartography; Geography; Remote Sensing/Photogrammetry; 공학일반; Deep learning; Autoencoder; Machine learning; Classification; Convolution neural network (CNN); Long short-term memory (LSTM); Remote sensing
• ISSN: 2366-3286; 2366-3294
• DOI: 10.1007/s41324-021-00425-2

A deep neural network (DNN), evolved from a traditional artificial neural network, has been seamlessly adapted for the spatial data domain over the years. Deep learning (DL) has been widely applied for a number of applications and a variety of thematic domains. This article reports on a systematic review of methods adapted in major DNN applications with remote sensing data published between 2010 and 2020 aiming to understand the major application area, a framework for model development and the prospect of DL application in spatial data analysis. It has been found that image fusion, change detection, scene classification, image segmentation, and feature detection are the most commonly used application areas. Based on the publication in these thematic areas, a generic framework has been devised to guide a model development using DL based on the methods followed in the past. Finally, recent trends and prospects in terms of data, method, and application of deep learning with remote sensing data are discussed. The review finds that while DL-based approaches have the potential to unfold hidden information, they face challenges in selecting the most appropriate data, methods, and model parameterizations which may hinder the performance. The increasing trend of application of DL in the spatial domain is expected to leverage its strength at its optimum to the research frontiers.