Initially, research in this domain was limited and exploratory, focusing mainly on documenting landslide occurrence in response to visible landscape changes. This early stage set the foundation for a more systematic investigation into the dynamics between shifting land use patterns and the triggering of landslides. As computational resources and remote sensing technologies improved, the field progressed into a development phase characterized by more sophisticated modeling techniques capable of incorporating diverse datasets. The explosion stage that followed marked a surge in publications, drawing on multi-modal scientometric analyses to unravel complex interactions and to enhance predictive capabilities.

The current body of literature clusters into three primary research streams, each addressing distinct yet interconnected facets of landslide science. The first cluster emphasizes susceptibility modeling, leveraging data-driven approaches to predict where landslides are most likely to occur. Advanced statistical methods and machine learning algorithms dominate these efforts, enabling researchers to handle vast arrays of geospatial data. The second stream examines the influence of climate change on landslide occurrence, investigating how shifting precipitation patterns, temperature variations, and extreme weather events exacerbate slope instability. The third area of focus delves into the micro-scale mechanisms that trigger small landslides, often emphasizing soil properties, root reinforcement, and hydrological changes at the slope level.

Despite the proliferation of sophisticated models, the predominant reliance on data-driven techniques has brought forth challenges related to interpretability and physical realism. Many studies prioritize optimizing prediction accuracy over understanding the mechanistic basis of LUCC-induced landslide susceptibility. This emphasis on computational performance sometimes obscures the causal pathways linking land transformations—such as deforestation and urban expansion—to increased landslide risks. Integrating physical processes into predictive models emerges as a key frontier for future research, promising to illuminate the underlying interactions between human activities and geomorphological responses.

Among the myriad anthropogenic drivers, deforestation and urban growth stand out as the most significant contributors to heightened landslide susceptibility. The removal of vegetation cover eliminates critical root networks that stabilize soils, while urban expansion alters natural drainage patterns, load distributions, and land surface conditions. However, the impact of changes in cultivated land, shrubland, and grassland is more nuanced and context-dependent. These land use types can both mitigate and exacerbate landslide risks, depending on factors such as soil type, slope gradient, regional climate, and land management practices, underscoring the need for localized studies tailored to specific environmental conditions.

A glaring limitation in the extant research base is the narrow focus on model comparison and optimization, often at the expense of deeper theoretical exploration. Discussions around the dynamic characteristics of the LUCC-landslide relationship remain superficial, limiting our ability to forge comprehensive, causally informed frameworks. This gap restricts the usefulness of models in informing land use policy and risk management decisions, impeding progress toward sustainable landscapes that balance human development and natural hazard mitigation.

Furthermore, there is an evident spatial bias in the coverage of landslide risk areas within the literature. Many high-risk regions experiencing rapid land use changes or frequent landslide events are underrepresented. This uneven geographic focus hampers global understanding and weakens disaster preparedness in some of the world’s most vulnerable communities. Expanding studies into these neglected zones is vital, requiring enhanced data collection efforts and collaborations across disciplines and borders.

The temporal dimension of LUCC effects on landslides remains another underexplored territory. Existing research often treats land cover changes and landslide occurrences as static or immediate phenomena, neglecting delayed or cumulative impacts that unfold over years or decades. Understanding these time-lag effects is essential for developing predictive models that can anticipate future hazards rather than merely reacting to past changes. Longitudinal studies incorporating historical datasets and time-series analyses are critical for uncovering these temporal dynamics.

Interdisciplinary integration also suffers from a lack of attention to the interactions between different land use types and their combined influence on landslide processes. Few studies systematically analyze how mosaics of agricultural fields, forests, urban zones, and natural habitats interact with climatic and geological factors to influence slope stability. This complexity calls for holistic frameworks that can accommodate the coupled natural-human systems driving landslide susceptibility.

In response to these challenges, researchers have put forward a sustainable land management framework that emphasizes the interconnectedness of disaster effects, land use patterns, and socioeconomic conditions. This comprehensive approach aims to harmonize land use planning with risk governance, providing policymakers with actionable strategies to mitigate landslide risks while promoting ecosystem conservation and social security. By integrating disaster science with socio-economic planning, this framework holds promise for fostering resilience in landscapes increasingly threatened by both anthropogenic and natural forces.

The proposed framework encourages the adoption of land use policies grounded in scientific evidence, tailored to the unique characteristics of regions, and responsive to ongoing environmental changes. It advocates for adaptive governance models that incorporate continuous monitoring, public participation, and cross-sector collaboration to dynamically manage landslide vulnerabilities in the face of climatic variability and land development pressures.

Ultimately, the trajectory of LUCC-landslide research over the last 20 years illuminates the critical need for an interdisciplinary, data-informed, and physically grounded science. The fusion of hazard mapping techniques with risk governance paradigms represents a paradigm shift, moving beyond hazard identification towards proactive, strategic risk management. This evolution reflects broader trends in environmental science where complex socio-ecological systems demand integrated approaches to understanding and mitigating natural disasters.

Looking forward, future studies will need to prioritize the incorporation of robust physical models into data-driven frameworks, closing the gap between prediction and explanation. Expanding the spatial and temporal scope of research will enhance the global applicability of findings and improve risk assessments in rapidly evolving landscapes. More importantly, fostering dialogue across disciplines—ranging from geomorphology and climatology to sociology and urban planning—will strengthen the foundations of sustainable land management strategies designed to minimize landslide hazards.

This comprehensive review serves not only as a synthesis of two decades of scientific progress but also as a call to action. Addressing the highlighted limitations and embracing interdisciplinary, multi-modal approaches can propel the field toward transformative breakthroughs. As landslides continue to threaten lives, infrastructure, and ecosystems worldwide, the integration of land use science with disaster risk governance becomes not just an academic pursuit but an urgent societal imperative.

The insights gleaned from this vast body of research underscore the complexity inherent in managing natural hazards amidst accelerating landscape transformations. They reveal that successful mitigation depends on nuanced, context-sensitive understandings of how human activities modulate geomorphic processes. By advancing this frontier, science can better inform policies that protect communities, safeguard ecosystems, and guide sustainable development in an era of unprecedented environmental change.

In sum, the evolution from hazard mapping to governance highlights an interdisciplinary odyssey marked by technological advances, deeper ecological knowledge, and escalating socio-political challenges. The journey captured through scientometric analysis is a testament to scientific innovation and a beacon for future research directions capable of transforming how societies coexist with landslide risks.

Subject of Research:
Impacts of land use/cover change (LUCC) on landslide susceptibility and the evolution of related research over 20 years based on multi-modal scientometrics.

Article Title:
From hazard mapping to risk governance: 20-year trajectory of land use/cover change impacts on landslide susceptibility via multi-modal scientometrics.

Article References:
Zhu, H., Zhu, X., Xu, Q. et al. From hazard mapping to risk governance: 20-year trajectory of land use/cover change impacts on landslide susceptibility via multi-modal scientometrics. Humanit Soc Sci Commun 12, 1609 (2025). https://doi.org/10.1057/s41599-025-05831-7

Image Credits:
AI Generated

The Indian western coastline, stretching along the Arabian Sea, is characterized by its complex geomorphology, varied climatic patterns, and a patchwork of geological formations susceptible to landslide occurrences. Historically, this region has witnessed devastating landslides resulting from monsoonal rains, seismic activities, and anthropogenic interventions such as deforestation and unregulated construction. Traditional hazard mapping techniques have struggled to capture the multidimensional interplay of these factors. However, the integration of sophisticated geospatial data layers, ranging from topographic variables to soil properties and rainfall intensity, paired with interpretative AI models, offers a systemic approach to unraveling these complexities.

At the core of this study lies the use of eXplainable Artificial Intelligence—an emergent subset of AI research focused on transparency and interpretability. While conventional machine learning models often operate as "black boxes," producing predictions without intelligible rationale, XAI elucidates the reasoning pathways behind each prediction. This transparency is crucial when decisions influence public safety and infrastructure development. The authors leveraged this technology to create a landslide susceptibility model that policymakers and local authorities can trust, assess, and deploy with confidence, paving the way for data-driven mitigation strategies.

To generate their susceptibility map, the research team first amassed a diverse compendium of spatial datasets. These included Digital Elevation Models (DEMs) depicting topographic gradients, lithological maps outlining rock types, land-use and land-cover (LULC) datasets, soil texture classifications, rainfall intensity and distribution patterns, and historical landslide inventory records. Each dataset was carefully pre-processed and standardized, ensuring compatibility and reducing uncertainties commonly encountered in multi-source geospatial modeling.

The methodological innovation arises from the fusion of these geospatial layers via an Ensemble Learning framework augmented by XAI methods. Ensemble Learning amalgamates the strengths of multiple machine learning algorithms, thereby enhancing predictive accuracy and robustness compared to singular models. Simultaneously, the researchers implemented Shapley Additive Explanations (SHAP) to attribute and rank the influence of individual factors on landslide susceptibility, thereby revealing the underlying drivers of slope instability in an intuitive manner.

Applying this combined approach, the model identified slope steepness, rainfall intensity, and soil texture as the triad of dominant contributors to landslide risk in the western coastal districts. Steeper slopes exhibited significantly elevated susceptibility, corroborating the classical geomorphological understanding that gravitational pull intensifies failure potential. Moreover, seasonal monsoon rains were found to modulate risk dynamically, underscoring the need for temporal-aware hazard assessments. These insights not only confirm empirical knowledge but also quantitatively establish the weight of each factor in triggering landslides.

Apart from validating known causes, the model uncovered surprising interactions among environmental variables. For instance, the presence of certain soil types in conjunction with moderate slopes amplified susceptibility more than previously anticipated. This nuanced interdependence highlights the inadequacy of linear heuristics in hazard prediction and demonstrates the strength of AI models in uncovering complex, nonlinear relationships.

The geospatial visualization outputs from the study provide a continuous susceptibility gradient, delineating high-risk zones that demand immediate attention for disaster mitigation measures such as slope reinforcement, afforestation, and early warning systems. These susceptibility maps enable planners to optimize resource allocation by prioritizing critically vulnerable areas, thereby reducing economic losses and safeguarding human lives. Furthermore, the transparent nature of the XAI models facilitates stakeholder engagement by allowing non-specialists to comprehend the basis of risk classification, enhancing community resilience.

This research also accentuates the evolving paradigm in geohazard science where interdisciplinarity drives innovation. By combining remote sensing technology, geoinformatics, geology, and artificial intelligence within a transparent framework, the authors exemplify how scientific collaboration transcends traditional disciplinary boundaries. Their rigorous analytical pipeline—from data acquisition, preprocessing, AI model training, to explainability assessment—sets a new benchmark for geospatial hazard studies worldwide.

The implications of this research extend beyond the immediate geographic focus. Coastal regions across the tropics face similar challenges linked to steep topography, intense precipitation, and human encroachment. Consequently, the methodologies and insights generated in this study could be adapted to other vulnerable regions, fostering global advancements in landslide risk mapping. As climate change intensifies weather extremes, such predictive tools become indispensable components of disaster risk reduction strategies.

Moreover, the incorporation of eXplainable AI addresses an often-overlooked challenge in applying AI for natural hazards: the lack of interpretability undermines trust and operational acceptance. By illuminating how each factor causally influences risk predictions, Shetkar and colleagues enable transparent knowledge transfer and iterative model refinement—crucial prerequisites for long-term monitoring and management applications.

From a technical standpoint, the study also makes important contributions to the geospatial data science community. Their approach addresses common data challenges, including multicollinearity among input variables, spatial autocorrelation, and data sparsity in landslide occurrence points. By employing robust feature selection and validation strategies, the model ensures generalizability, mitigating the risk of overfitting and enhancing reliability across spatial scales.

The publication rigorously details the performance metrics of the developed models, reporting high values in Area Under the Curve (AUC), precision, recall, and F1-score. This robust validation framework was complemented by spatial cross-validation schemes to ensure stability and replicability of results—critical for credible scientific outcomes. The model’s success attests to the promising synergy between explainable AI paradigms and geospatial hazard modeling.

In sum, this pioneering work offers a crucial synthesis of technological innovation, scientific rigor, and practical applicability. It stands as a beacon for future multidisciplinary endeavors seeking to harness AI’s potential for environmental hazard assessment while maintaining transparency and accountability. The impact of such research is profound, promising safer human settlements, smarter infrastructure design, and enhanced climate resilience in landslide-prone regions of India and potentially beyond.

As the scientific community continues to grapple with the dual challenges of increasing natural disasters and burgeoning data complexity, the work by Shetkar, Das, Desai et al. forms a vital cornerstone for integrating machine intelligence with earth system sciences. Their holistic, transparent AI-assisted workflow aligns perfectly with the urgent need for actionable intelligence, empowering governments and communities to navigate the precarious terrain of climate change and geological hazards.

Looking forward, the research suggests intriguing avenues for enhancement such as incorporating real-time monitoring data from IoT sensors, advancing temporal predictive modeling, and integrating socio-economic vulnerability indices. Such expansions could amplify the utility of landslide susceptibility maps into comprehensive risk assessment frameworks, fostering truly holistic disaster management approaches.

Ultimately, this fusion of geospatial methodologies and eXplainable AI not only enhances our understanding of landslide dynamics but also democratizes access to critical information, equipping all stakeholders with the knowledge required to foster resilience in the face of evolving natural threats. The map that emerges from this study is more than cartography—it is a roadmap toward safer futures shaped by insight, transparency, and innovative science.

Subject of Research: Landslide susceptibility mapping using geospatial techniques and eXplainable Artificial Intelligence in western coastal districts of India.

Article Title: Landslide susceptibility mapping for western coastal districts of India using geospatial techniques and eXplainable artificial intelligence.

Article References:

Shetkar, D.A., Das, B., Desai, S. et al. Landslide susceptibility mapping for western coastal districts of India using geospatial techniques and eXplainable artificial intelligence.
Environ Earth Sci 84, 327 (2025). https://doi.org/10.1007/s12665-025-12343-9

Image Credits: AI Generated