Global Intelligent Ecological Mapping: AI, Big Data, and Digital Earth Approaches for Sustainability

The accelerating impacts of climate change, biodiversity loss, and pollution highlight the urgent need for high-resolution, near-real-time ecological monitoring. Advances in remote sensing, artificial intelligence (AI), and cloud computing now enable a shift toward a new paradigm of Global Intelligent Ecological Mapping. By integrating multi-source Earth observation data, socio-ecological indicators, and digital earth technologies, this emerging approach provides a powerful framework for monitoring ecosystem functions, assessing environmental risks, and supporting policy-making for sustainable development.

This Research Topic seeks to establish a platform for cutting-edge research on intelligent ecological mapping, with a special emphasis on global-scale applications, methodological innovations, and decision-oriented outcomes. It will also highlight initiatives such as the Global Intelligent Ecological Horizon Project (GIEHP), which aims to provide a unified ecological grid and open-access data for the global scientific community.

Global environmental challenges—including climate change, biodiversity loss, and pollution—demand innovative approaches to monitor, assess, and manage ecosystems at multiple scales. Traditional ecological mapping methods often struggle to capture the complexity, dynamics, and spatial heterogeneity of socio-ecological systems. However, recent advances in AI, big data analytics, and cloud-based digital earth platforms offer unprecedented opportunities for producing high-resolution, near-real-time ecological maps.

This Research Topic aims to bring together interdisciplinary contributions that explore novel frameworks, datasets, and tools for Global Intelligent Ecological Mapping. By integrating remote sensing, street-level imagery, socio-economic indicators, and ecological models, the topic seeks to advance methodological innovation, present global and regional case studies, and highlight applications for ecosystem services, climate resilience, and sustainable development.

Ultimately, this initiative will promote open, scalable, and intelligent ecological mapping to support both scientific discovery and evidence-based policy-making.

We particularly encourage contributions that:

• Showcase methodological innovations in AI-driven ecological mapping, including machine learning, deep learning, and cloud computing platforms.

• Highlight global and regional applications related to ecosystem services, carbon dynamics, pollution exposure, climate risk, and biodiversity monitoring.

• Explore urban and socio-ecological dimensions, including street-level mapping, environmental perceptions, and health implications.

• Provide insights into policy, governance, and sustainable development, demonstrating how intelligent ecological mapping can inform the SDGs, COP climate agendas, and biodiversity frameworks.