Machine Learning for Biodiversity Monitoring from Remote Sensing and Citizen Science Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biodiversity loss is occurring at an unprecedented rate, threatening ecosystem services critical to food, water, and human health and well-being.Understanding species distributions is crucial for conservation policy.However, traditional species distribution modeling (SDM) methods focus on limited species or regions, leaving major knowledge gaps.A key barrier is the extensive effort needed for traditional monitoring.Remote sensing and citizen science offer opportunities to transform biodiversity monitoring and enable modeling complex ecosystems.This thesis introduces the task of mapping bird species to habitats by predicting encounter rates from satellite images and crowd-sourced citizen science data.We create a dataset with satellite images from the US and Kenya with labels derived from presenceabsence observation data from citizen science database eBird.We train baseline models and show that we can learn specific distribution patterns from these data.We also show that we can utilize the trained models to improve predictions in areas where data may be limited, specifically, where eBird checklists are limited.The released dataset -SatBird and pre-trained models enable scalable ecosystem modeling worldwide.i 6.4.2Multiple Hotspots scenario . . . . . . . .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it