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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.013 |
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; both teacher heads agree on what is shown here.
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".