Time-series displacement from high-resolution satellite Synthetic Aperture Radar (SAR) data using Sub-Pixel Offset Tracking (SPOT)
Bibliographic record
Abstract
Time-series displacements at Merapi volcano, Indonesia, from high-resolution SAR and sub-pixel offset tracking (SPOT). When using this dataset, please cite the authors: Bemelmans, Mark^1,2,+; Biggs, Juliet^1,2; Wookey, James^1; Poland, Michael^3. 1: School of Earth Sciences, University of Bristol, Bristol, United Kingdom.2: Centre for the Observation and Modelling of Volcanoes, Earthquakes, and Tectonics (COMET), United Kingdom.3: Cascades Volcano Observatory (CVO), U.S. Geological Survey (USGS), Vancouver, Washington, The United States.+: corresponding author, mark.bemelmans@bristol.ac.uk *Work is currently in preparation and will be updated when publication information is known.* Background Information: Synthetic Aperture Radar (SAR) data are collected through the Centre for Earth Observation Satellites (CEOS). The COSMO-Skymed (CSK) data are owned by the Italian Space Agency (ISO) and the TerraSAR-X (TSX) data are owned by the Deutsche Lüft- und Raumfarht Centrum (DLR). Additionally, the Digital Elevation Model (DEM) used to correct for parallax shift is from Thomas and Darmawan, 2021. (High-resolution Digital Elevation Model of Merapi summit in 2015 generated by UAVs and TLS and TanDEM-X. The data are processed using GAMMA-rs (Werner et al., 2000) using Sub-Pixel Offset Tracking (SPOT). SPOT uses image cross-correlation of small image patches to extract displacements (offsets) to sub-pixel precision. Details of the data processing can be found in [Bemelmans et al., in prep] This data set currently includes data from CSK (heading 168 deg., incidence angel 35.6 deg., image resolution 0.34 in slant range, 0.70 in azimuth) with the following dates: 20200910202009192020092620200927202010052020101220201013202011132020111420201122202012242020123120210101202101092021011620210117202101252021020120210202202102172021021820210302202103052021030620210314202103212021032220210330202104062021041920210422202104232021050120210508202105092021052120210609202107112021072020210727202107282021081220210813202108292021091020210913202109142021092220210926202109302021101520211016202110242021103120211101 We also have data from TSX (heading 191 deg., incidence angle 37.6 deg., image resolution 0.45 m in slant range, 0.17 m in azimuth). We have acquisitions from the following dates: 202011162020112720201208202012192021011020210201202102232021031720210408202104302021052220210602202106132021100120211023 For each acquisition we process pixel offsets to the 3 adjacent acquisitions. We then select the pixels for which we have 1 connected network of acquisition pairs that cover all the dates in the time series. We use (unweighted) least-squares inversion to extract the time series. This dataset contains the following two files: - CSK_SPOT_dsc_win13_ts_inv.csv (containing 282,270 points)- TSX_ST134_win13_ts_inv.csv (containing 48,291 points) CSK_SPOT_dsc_win13_ts_inv.csv contains the time series pixel offsets from the CSK data in the slant range and azimuth directions (in meters). TSX_ST134_win13_ts_inv.csv contains the time series pixel offsets from the TSX data in the slant range and azimuth directions (in meters). The Data has the following format: Row id | Longitude [deg. E] | Latitude [deg. N] | range disp. Date 1 [m] | azimuth disp. Date 1 [m] | range disp.Date 2 [m] | azimuth disp. Date 2 [m] | ...0 110.4641 -7.5363 0 0 0.003 0.0501 110.4639 -7.5363 0 0 0.004 0.0772 110.4638 -7.5364 0 0 0.047 0.0813 110.4637 -7.5364 0 0 0.054 -0.0274 110.4634 -7.5365 0 0 0.041 -0.0025 110.4633 -7.5365 0 0 0.037 0.022 Note: in this dataset, row id refers to the row index of the original dataset which was filtered for data we could not make a complete time -series for and is not of any use for further interpretation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".