Deep Learning Algorithms for Land Use Change Detection

Dear Colleagues,

The rapid growth of the human population in recent decades has increased the demand for residential lands, natural resources, and basic services. The increased shift of population from rural to urban areas leads to rapid urbanization, and this has resulted in huge increase in the amount of built-up areas. The identification and management of land-use changes related to these trajectories have become imperative for meeting the needs of growing populations. Remote sensing techniques can be used to monitor changes in land use and land cover. The data used includes images of land use, weather and soil conditions, and more. The massive volume of land data demands an effective big data analytics process, so that unknown and complex patterns can be discovered. Most of the existing research works make use of aerial and space-borne scene classification to determine land-use changes. Here, the images are labelled under predefined conditions such as vegetation, housing, service facilities, etc. The changes in land use and land cover are documented with the help of remote sensing techniques. Advances in technology have enriched the growth of remote sensing, but at the same time traditional approaches have become less appropriate for the detection of land-use and land-cover changes. In order to provide meaningful analysis of the huge amounts of data, big data analytics is increasingly used because of its ability to reveal patterns in complex situations. A major challenge for big data analytics in recent times has been the increase in data volumes. This increases the requirement for the development and use of advanced learning algorithms to find and interpret dynamic changes in land use.

Deep learning (DL) algorithms can efficiently handle huge volumes of data, and can classify them with increased accuracy. Examples of important land-use detection include DL algorithms like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). The input data passes across the multiple hidden layers through which the patterns are recognized and classified. Data collection, feature extraction, pattern recognition, data training, and classification are key features of these algorithms. 

Diversity in land use brings out the heterogeneity in the datasets. DL algorithms classify the datasets, which are then fed into low-level and high-level layers based on the level of data complexity. This enables algorithms to easily extract the core features of the datasets, which enhances the quality of image classification methods and can improve pre-processing and segmentation tasks, handle high-dimensional data, and perform well with limited datasets.

This Special Issue on “Deep Learning Algorithms for Land-Use Change Detection” encourages researchers and data scientists from various research backgrounds to present their novel ideas and algorithms for the detection of land-use change detection with DL techniques.

The topics of interest include but are not limited to the following:

• Convolutional neural networks (CNNs) for mapping land-cover changes;
• Deep learning (DL) methods for understanding the impact of growth in population and land usage;
• Modeling a deep belief network (DBN) to determine land-use change in urban areas;
• Big data for land-use change detection: challenges and opportunities;
• Quality assessment of land cover big data;
• Deep learning (DL) algorithms to evaluate landscape changes;
• Surveys on existing deep learning (DL) techniques to classify land use;
• Meta-analysis for core feature extraction of land cover in developed countries;
• Deep learning for resolving the challenges for land-use and land-cover change;
• Applications of deep learning and big data analytics in land-use and land-cover change detection;
• Case studies on global land cover monitoring and big data challenges;
• The role of convolutional neural networks (CNNs) in documenting land degradation;
• Recent advancements in deep learning (DL) for the quality assessment of soil, water, and air due to land-use change;
• Deep learning approaches to understanding the influences of population growth on land-use change;
• Land-use change classification using deep learning approaches;
• Deep learning and big data analytics for land detection and knowledge discovery.

Dr. Gunasekaran Manogaran
Dr. Hassan Qudrat-Ullah
Dr. Qin Xin
Guest Editors

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