Poverty mapping is increasingly important for monitoring Sustainable Development Goal 1 (SDG 1) of the United Nations 2030 Agenda, which aims to end poverty in all its forms everywhere. Yet timely and fine-resolution poverty estimation remains difficult because conventional census- and survey-based approaches are costly, infrequent, and often sparse precisely where deprivation is most severe. As poverty emerges from complex socioeconomic systems shaped by human mobility, social interactions, infrastructure, and economic activities, emerging computational methods and nontraditional data sources have created new opportunities for poverty estimation and mapping. At the intersection of statistical physics, complex systems science, and data science, these approaches enable poverty estimation at finer spatial and temporal resolutions. This review summarizes the main concepts of poverty and the principal frameworks used to measure it, and examines recent advances on poverty estimation and mapping using satellite imagery, mobile phone data, social media data, and multisource data fusion. The review also discusses persistent challenges related to representativeness, transferability across regions, interpretability, and uncertainty quantification. Finally, the review clarifies both the analytical promise and the practical limits of contemporary poverty mapping.
Monitoring biodiversity in agricultural landscapes is essential for addressing global biodiversity decline. Emerging remote sensing and artificial intelligence technologies enable large-scale, low-impact and cost-effective assessments. This chapter provides a concise review of remote sensing applications for farmland biodiversity monitoring, structured around the Essential Biodiversity Variable framework. We examine plants, animals, fungi and microorganisms, and their habitats in productive and semi-natural areas. Spaceborne and airborne sensors effectively assess agroecosystem structure and functioning, and detect plants and large mammals with distinct spectral or phenological traits. UAV imagery and camera traps offer higher resolution for monitoring smaller or cryptic species. Remote sensing also supports indirect methods, such as species distribution modeling, to infer the occurrence of species that are difficult to detect. Key challenges include technological, methodological and operational limitations. Integrating multi-platform data, establishing shared benchmarks, and broadening taxonomic coverage, supported by policy incentives and farmer-friendly technologies, are essential for scaling biodiversity monitoring and promoting sustainable strategies that reconcile conservation with agricultural production.
Providing accurate urban pedestrian volume estimates is essential for optimizing public infrastructure, improving services, and enhancing active travel experiences. However, collecting large-scale pedestrian mobility data is prohibitively expensive and often impractical in real-world scenarios. Pedestrian volume observations on road networks are typically sparse: only a small fraction of links have sensor-derived observations, while most links remain sensor-uncovered with no observations at all. Existing data-driven prediction and imputation methods rely heavily on spatially dense observations, while directly applying these models to extrapolate to numerous unobserved links may result in unreliable generalization. Moreover, prior research lacks analysis and corresponding mechanisms to adaptively adjust how transportation and land-use features (e.g., local road conditions, transit accessibility, and land-use patterns) are leveraged under observation sparsity. To address these challenges, we propose a novel State-conditioned Mamba-Hypernetwork Framework (SMHF) to estimate urban pedestrian volumes with minimal available observations. SMHF formulates estimation as a state-conditioned stepwise process: the estimator is updated at each step conditioned on an evolving estimation state sequence that dynamically fuses transportation and land-use features, graph-structured context, current pedestrian volumes with their observed/estimated status, and estimation progress. SMHF demonstrates superior performance under observation sparsity over baselines and state-of-the-art models on a real-world pedestrian volume dataset through supervised evaluation, and an independent on-site manual-count validation confirms the practical applicability of SMHF’s citywide inference on sensor-uncovered links. A sparsity-specific interpretability study provides insights into urban transportation and environmental factors associated with pedestrian activity. It also shows a consistent monotonic increase in feature-importance scores as observations become sparser, indicating a growing reliance on static features when observations are limited and highlighting the need for adaptive feature utilization under observation sparsity.
To better satisfy the uneven spatiotemporal distribution of passenger demand on the Y-type metro line, the concept of flexible train composition is explored, where the train compositions can be changed flexibly at a joint station and terminal stations connected to depots. A mixed-integer nonlinear programming model is developed to jointly optimize train timetables, rolling stock circulation plans, and flexible train compositions on a Y-type metro line. Using standard linearization techniques, the model is reformulated as a mixed-integer linear programming (MILP) model with an objective that balances operating cost and passenger service quality. To incorporate short-term demand variations and support real-time operations, the integrated MILP is embedded into a Model Predictive Control (MPC) framework, which updates timetabling decisions dynamically based on demand forecasts. However, solving the resulting MPC-based MILP in real time is computationally challenging due to the large number of binary decision variables. To overcome this limitation, we propose a learning-based optimization framework that integrates offline learning with online mixed-integer optimization. The learning module is trained offline on historical MPC solutions and is designed to selectively predict a subset of high-impact binary decisions, including train composition and routing choices, thereby reducing the combinatorial complexity of the online optimization problem. The predicted decisions are then fixed in an online MILP to optimize the remaining variables while preserving feasibility and consistency across rolling horizons. In addition, a feasibility-efficiency balancing strategy is introduced through selective decision prediction and feasibility-aware penalized training, which reduces infeasible learning outputs. Numerical experiments based on real-world operational data from Guangzhou Metro Line 14 show that the proposed learning-based MPC framework achieves real-time timetabling, while maintaining solution quality comparable to a full MPC-based optimization benchmark, with only a marginal loss in feasibility. Computational results demonstrate the effectiveness of integrating learning and MPC for real-time timetable optimization.
Mycorrhizal type of the dominating vegetation is one of the main drivers of soil functioning, especially across forest ecosystems. While the topsoils (above 30 cm) are mostly influenced by the soil organic matter that comes from the leaf litter decomposition, and therefore is mostly determined by the plant species, the subsoil is highly affected by the rhizosphere processes and directly—by the activity of the soil microbiome, including bacteria and fungi. The current research was aimed at testing how the mycorrhizal type of tree stand affects the vertical distribution of soil nematode communities, driven by the abundance of organic resources and soil parameters. To address this, we assessed the density of individual taxa and feeding types of nematodes along a one-meter soil profile in ectomycorrhiza (ECM) and arbuscular mycorrhiza (AM) dominated soils of a temperate experimental forest (MyDiv experiment). We show that nematode community composition differed between ECM and AM-dominated topsoils, while the subsoil communities were similar. At the same time, the dominating mycorrhizal type drove the density distribution across the one-meter soil profile for individual taxa, including some bacterivores, fungivores, and omnivores, indicating the specific feeding preferences of individual nematode taxa. In general, our data represents the slow change in nematode communities that occurs with the soil development after reforestation, simultaneously reflecting the strong legacy effect of post-agricultural soil.
