arXiv subjects

Zia Mehrabi

Publications and source records attributed to Zia Mehrabi.

5 recordsLinked to original sources

arXiv2026

Contextual Geospatial Features for Identifying Informal Environmental-Health Hazards Undetectable from Satellites: A ULAB Case Study

Reliable, scalable detection of informal, small-scale environmental-health hazards (used lead-acid battery (ULAB) recycling, household-scale e-waste burning, indoor mercury amalgamation, brick kilns, small tanneries) remains an unsolved problem. These operations are invisible to satellites and absent from formal registries, yet disproportionately harm low-income populations in low- and middle-income countries. This paper articulates the problem class and explores a possible response: contextual geospatial features, with case-specific feature design informed by domain expertise. We use ULAB recycling as a demonstration case, drawing on 164 verified sites in Bangladesh and India from Pure Earth's Toxic Sites Identification Programme. At this sample size, five-fold cross-validation on the training set cannot statistically distinguish the engineered contextual features from a simple two-feature socio-demographic baseline. The added value only becomes visible when we evaluate outside the training set. On 172 held-out informal-recycling sites in non-NCR India and Bangladesh, the model assigns scores several times higher than to matched random urban controls; and on an independent set of 131 regulatory-confirmed formal recyclers, informal sites score materially higher than formal ones in non-NCR India, indicating that the model is picking up informal-recycler-specific structure rather than generic industrial signal. We frame these results as exploratory rather than confirmatory: label sparsity, gaps in point-of-interest coverage, and untested transfer beyond South Asia all remain open. We close with seven open problems and invite the environmental-health and geospatial machine-learning communities to engage with informal-hazard detection as a class of problems worth solving.

stat.AP↗

arXiv2024

Geographic distribution of the global agricultural workforce every decade for the years 2000-2100

Agricultural workers play a vital role in the global economy and food security by cultivating, transporting, and processing food for populations worldwide. Despite their importance, detailed spatial data on the global agricultural workforce have remained scarce. Here, we present a new gridded dataset that maps the global distribution of agricultural workers for every decade over the years 2000-2100, distributed at 0.083$\times$0.083 degrees resolution, roughly $\sim$10km$\times$10km at the Equator. The dataset is developed using an empirical modeling framework relying on generalized additive mixed models (GAMMs) that integrate socioeconomic variables, including gross domestic product per capita, total population, rural population size, and agricultural land use. The predictions are consistent with Shared Socio-economic Pathways and we distribute full time series data for all SSPs 1 to 5. This dataset opens new avenues for future research on labour force health, productivity and risk, and could be very useful for developing informed, forward-looking strategies that address the challenges of climate resilience in agriculture. The dataset and code for reproducing it are available for the user community [publicly available on publication at DOI: 10.5281/zenodo.14443333].

stat.AP↗

arXiv2024

Mapping waterways worldwide with deep learning

Waterways shape earth system processes and human societies, and a better understanding of their distribution can assist in a range of applications from earth system modeling to human development and disaster response. Most efforts to date to map the world's waterways have required extensive modeling and contextual expert input, and are costly to repeat. Many gaps remain, particularly in geographies with lower economic development. Here we present a computer vision model that can draw waterways based on 10m Sentinel-2 satellite imagery and the 30m GLO-30 Copernicus digital elevation model, trained using high fidelity waterways data from the United States. We couple this model with a vectorization process to map waterways worldwide. For widespread utility and downstream modelling efforts, we scaffold this new data on the backbone of existing mapped basins and waterways from another dataset, TDX-Hydro. In total, we add 124 million kilometers of waterways to the 54 million kilometers already in the TDX-Hydro dataset, more than tripling the extent of waterways mapped globally.

cs.CV↗

arXiv2024

Deep learning waterways for rural infrastructure development

Surprisingly a number of Earth's waterways remain unmapped, with a significant number in low and middle income countries. Here we build a computer vision model (WaterNet) to learn the location of waterways in the United States, based on high resolution satellite imagery and digital elevation models, and then deploy this in novel environments in the African continent. Our outputs provide detail of waterways structures hereto unmapped. When assessed against community needs requests for rural bridge building related to access to schools, health care facilities and agricultural markets, we find these newly generated waterways capture on average 93% (country range: 88-96%) of these requests whereas Open Street Map, and the state of the art data from TDX-Hydro, capture only 36% (5-72%) and 62% (37%-85%), respectively. Because these new machine learning enabled maps are built on public and operational data acquisition this approach offers promise for capturing humanitarian needs and planning for social development in places where cartographic efforts have so far failed to deliver. The improved performance in identifying community needs missed by existing data suggests significant value for rural infrastructure development and better targeting of development interventions.

cs.CV↗

arXiv2024

Satellite monitoring uncovers progress but large disparities in doubling crop yields

High-resolution satellite-based crop yield mapping offers enormous promise for monitoring progress towards the SDGs. Across 15,000 villages in Rwanda we uncover areas that are on and off track to double productivity by 2030. This machine learning enabled analysis is used to design spatially explicit productivity targets that, if met, would simultaneously ensure national goals without leaving anyone behind.

cs.CY↗