Applying global NASA data to local planning for extreme heat in Prince George’s County, Maryland
Bibliographic record
Abstract
As demand grows for high-quality public information around the increasing societal impacts of extreme weather, making scientific data products more actionable for practitioners has become a top priority for NASA. In early 2024, several of us from NASA Goddard Space Flight Center began collaborating with Prince George’s County, Maryland staff to apply space-based observations to help inform local decisions around building resilience to environmental stressors. Prince George’s County is home to Goddard Space Flight Center hosting a comprehensive array of Earth observing satellite and modeling products and expertise to advance understanding of Earth as a system, providing critical long-term data records over space and time. Scientific and academic communities have primarily used these global data products to advance fundamental understanding of Earth processes and their changes, rather than informing practical decisions. Here we describe a demonstration to bridge that gap, initially around reducing exposure of humans to extreme heat. Working with agency staff from the Prince George’s County Department of Environment and the Prince George’s County Health Department, NASA shared satellite-derived land surface temperatures from Aqua-MODIS and Landsat as well as land cover classifications to analyze seasonal temperature trends with land use changes over the past few decades. Prior to partnering with NASA, the county was using sparse in situ temperature records, extremely valuable and essential for ground-truthing satellite data, but limited spatially and temporally. The addition of space-based data augments these few ground-based measurements with a broad view of spatial and temporal temperature trends that can be layered with additional county data to help identify areas of increased heat with land use change patterns. We also compared the temperature trends to records of annual heat-related emergency room visits. By combining environmental data with local health impacts, the County plans to pinpoint heat-related vulnerability patterns over time. This unique combination of global and local data serves to pilot science-based criteria to inform and prioritize projects and programs to reduce heat exposure of Prince George’s County’s workers and residents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".