Dynamic Geo-Visualization of Urban Land Subsidence and Land Cover Data Using PS-InSAR and Google Earth Engine (GEE) for Spatial Planning Assessment
Why this work is in the frame
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Bibliographic record
Abstract
The North Java coastal area, known as the Pantura region, is experiencing significant land subsidence, with certain areas sinking up to 10 cm per year. Pekalongan is among the most affected, with subsidence rates between 10 and 19 cm annually, mainly due to groundwater extraction, sediment compaction, and coastal erosion. Other coastal cities, like Semarang and Demak, show rates averaging 4 to 10 cm per year. This rapid subsidence is due to favorable geological conditions and ongoing urban development. This study investigates land subsidence in Pekalongan using the PS-InSAR method and dynamic visualization of time-series land cover data. PS-InSAR was applied to 45 scenes from ALOS-2 PALSAR-2 to monitor subsidence from 2014 to 2022. The results were validated with in situ subsidence benchmarks. Urban development dynamics were analyzed through land cover and land use change (LULC) and population density over the same period, using the GLC_FCS30D dataset in the GEE to detect non-natural LULC. The PS-InSAR results indicated that over 60.9% of investigation points experienced subsidence, up to 100 cm between 2014 and 2022. Ground validation showed an 83% agreement with PS-InSAR results. A statistical analysis of LULC from 2014 to 2022 did not show significant built-up area development, but the extension of salt marshes and water bodies indicated subsidence expansion. The population density reached 6873 people per square km by 2022, causing extensive groundwater use for domestic and industrial purposes, further aggravating the subsidence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it