Train models to explain how sea-level changes affect coastal inundation risk by comparing baseline and hypothetical scenarios.

This dataset contains high-definition videos for analyzing and optimizing watering operations.

Use GIS coverage and boundary data for fog and low clouds to locate and analyze low-visibility weather regions.

The Adverse Weather Road Image Classification Dataset focuses on enhancing the performance of intelligent transportation systems under complex meteorological conditions.

Diagnose possible triggers of new convection from satellite time-series clues, with evidence and uncertainty clearly identified.

Train models to trace complex counterfactual causes linking external nutrient reductions, sediment release, and delayed water-quality responses.

Develop waste recognition and intelligent sorting models with multicategory waste images and associated annotation metadata.

Build traceable planning and compliance Q&A by locating key provisions in floodplain policies and flood protection regulations.

Train models to reason about how coastal protection changes inundation risk and what residual impacts remain through paired scenarios and explanations.

The 2020 Weather Indicator Time Series Dataset provides precise weather change data for environmental analysis and predictive models.

Explore four categories of waste in everyday kitchen scenes with annotations for training and evaluating recognition and classification models.

This dataset records the full video of a desert convoy crossing, supporting environmental monitoring and autonomous driving research.

Train models to reason about how changes to watershed nutrient sources may affect water quality, with explicit assumptions and evidence boundaries.

Train models to trace causal links between controlled nitrogen and phosphorus input changes, nutrient limitation, algal responses, and water-quality effects.

Fine-grained annotations of change, negation, and uncertainty in satellite reports help improve model understanding and structured outputs.

Build and evaluate retrieval and answering systems with evidence-linked questions grounded in extreme heat emergency plans.

Build river waste recognition and water pollution monitoring models with annotated UAV aerial imagery.

Train and evaluate RAG systems on flood emergency procedures with question-answer records grounded in source passages and supporting evidence.

Train models to explain how hypothetical precursor emission controls may affect ozone and why those effects depend on chemistry and environmental conditions.

The School Area Noise Pollution Assessment Dataset focuses on analyzing and evaluating campus noise levels.