Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment

Dear Colleagues,

Agriculture, natural disasters, and ecological environment management are critical areas linked to sustainable development and human well-being. The integration of artificial intelligence (AI) models with spatiotemporal data (SD) has emerged as a transformative approach, providing powerful tools for data collection, analysis, and decision-making in these fields. This Topic aims to highlight the latest advancements and applications of AI combined with SD, showcasing how these technologies can enhance our understanding and management of agricultural systems, mitigate the impacts of natural disasters, and protect ecological environments. Traditional methods in the fields of agriculture, disaster management, and ecological monitoring often involve complex spatial and temporal data, making them time-consuming and resource-intensive. The advent of AI models combined with SD has provided researchers and practitioners with the ability to collect, process, and analyze large volumes of data efficiently. These technologies assist with the accurate and timely monitoring of agricultural processes, prediction of natural disasters, and assessment of environmental conditions. This topic seeks to gather cutting-edge research that demonstrates the innovative applications of AI combined with SD related to agriculture, natural disasters, and ecological environment management. We aim to cover a broad spectrum of topics, including but not limited to the following:

1. Agricultural Optimization and Sustainability:

• Development and validation of AI models for crop yield prediction using SD.
• Integration of AI for improved irrigation and fertilization management.
• Assessment of climate change impacts on agricultural productivity using long-term spatiotemporal datasets.

2. Natural Disaster Management:

• Real-time monitoring and prediction of natural disasters (e.g., floods, earthquakes, landslides) using AI and SD.
• Development of early warning systems for natural disasters using integrated AI approaches.
• Post-disaster assessment and recovery planning with AI and SD.

3. Ecological Environment Monitoring:

• Assessment and mapping of ecological environments using AI and SD.
• Monitoring biodiversity and ecosystem health through AI-driven analysis of satellite imagery and sensor data.
• Prediction of environmental changes and their impact on ecosystems using AI models.

4. Urbanization and Land Use Change：

• Intelligent mapping and analysis of urban expansion and land use dynamics using AI and SD.
• Identification and classification of urban functional zones, impervious surfaces, and built-up areas using deep learning techniques.
• Scenario-based simulation and prediction of future urban land use changes driven by AI-integrated cellular automata and spatial models.

We invite researchers, practitioners, and scholars to submit original research articles, review papers, and case studies that highlight the applications of AI combined with SD in the fields agriculture, natural disasters, and ecological environment management. Submissions should provide clear evidence of the use of these technologies to address specific challenges in the field, demonstrate innovative methodologies, and present significant findings that advance the state of the art. The integration of AI and SD offers unparalleled opportunities to enhance our understanding and management of agricultural, disaster, and ecological systems. This Topic will serve as a platform for sharing the latest advancements and fostering collaboration among researchers and practitioners in this dynamic field. By showcasing innovative applications and methodologies, we hope to contribute to the development of more effective strategies for sustainable development and disaster mitigation. We look forward to receiving your contributions and to the exciting advancements that this Topic will bring to the fields of agriculture, natural disasters, and ecological environment management.

Dr. Heng Lu
Dr. Xiaoai Dai
Dr. Lei Ma
Topic Editors