LAND COVERAGE ANALYSIS OF PAKISTAN USING SATELLITE IMAGERY
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
Abstract. Pakistan has a unique landscape geographically due to its strategic geo-political importance. It has played a vital role in global climate and politics. There are various semantic segmentation studies performed on remote sensing high-resolution imagery of various urban and rural areas into major classes of buildings, vegetation, water, and roads. These analyses have supported the land coverage study, which can facilitate urban infrastructure management, forestry, disaster management, and climate challenges. Recent climate reports have confirmed the importance of these studies, especially for Pakistan. It’s a critical location for the global south to observe the climate catastrophe. This research will focus on three major cities of Islamabad, Karachi, and Quetta and semantically segment the satellite imagery to study the land coverage. Our research contributes the dataset from major cities of Pakistan and compare the performance of state-of-the-art semantic segmentation networks to evaluate the dataset. Benchmark can help in selecting a highly effective deep learning network and generalizing those networks on our prepared dataset. Dataset can be downloaded from here: https://github.com/Abdullah-Sabir/Pakistan-Land-Coverage-Analysis-Dataset
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 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 it