Dear Colleagues,

The intersection between spatial data science and remote sensing holds the key to the solution of many world challenges. It is urgent to harness the ever more present and powerful land observation technology to mitigate problems such as wildland fires, deforestation, ocean and water resources monitorization, resource depletion, sustainable urbanization, and human settlements. This is pivotal to creating a more harmonious and sustainable future. Much of the quality of the interactions between humans and the environment relies on our ability to understand the impact of human activities on the environment. This is the only way to design and implement strategies that can lessen our environmental footprint. To achieve this, it is indispensable to tightly couple remote sensing, as a data acquisition technology, and spatial data science, as the appropriate toolbox, to make sense of spatially distributed data. There is urgency in bringing together these two fields, weaving successful strategies to ensure environmental sustainability while promoting sensible growth and development. Doing more with less will be essential for harmonious and balanced interactions between humans and the environment. However, this can only be achieved through the development of an information-rich environment that can support decision-making based on evidence and knowledge.

This Special Issue will accept original research papers on both applications and methodologies, as long as they focus on the intersection between remote sensing and spatial data science. The empirical outlets are within a wide range and often multidisciplinary, such as

• Remote sensing for the smart city;
• Urbanization and settlements;
• Land cover and land use;
• Agriculture;
• Wildland fire;
• Climate change;
• Ocean monitorization;
• Deforestation;
• Archaeological prospection;
• Heritage preservation;
• Regional impact analysis;
• Smart cities;
• Spatial analysis of LIDAR data.

Concerning methodological approaches, the emphasis on spatial data science includes but is not limited to the following:

• Automatic classification;
• Machine learning;
• Deep neural networks;
• Time series;
• Data fusion;
• Outlier detection;
• Change detection;
• Efficient training sets;
• Learning from imbalanced data;
• Regression techniques;
• Quasi-experimental methods;
• Data preprocessing;
• Feature extraction and engineering;
• Land use/land cover change;
• Mixed methods.

Dr. Fernando Bação
Prof. Dr. Eric Vaz
Dr. Bruno Damasio
Guest Editors

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