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Dev - Naming Convention for Sentine-1 Added.
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eciraci authored Jan 20, 2024
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196 changes: 195 additions & 1 deletion README.md
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# IRIDE_template
IRIDE Service Segment Lot 2
---
[![Language][]][1]
[![License][]][1]

---
### Naming Convention and Metadata

---

#### Naming Convention - SE-S3-01
**S3_01_SNT_01**

Data delivery will be at burst level. Each burst will generate a product.
Naming convention:
- Track + Orbit Direction - 4 chars (3 + "A"/"D")
- Burst - 4 chars
- Swath - 3 char ("IW"+1)
Example:
ISS Product Name: ISS_S301SNT01_20180101_20220101_015A0250IW2_01.extension

---
**S3_01_SNT_02**

Data delivery will be at burst level. Each burst will generate a product.
Naming convention:
- Track + Orbit Direction - 4 chars (3 + "A"/"D")
- Burst - 4 chars
- Swath - 3 char ("IW"+1)
Example:
ISS Product Name: ISS_S301SNT02_20180101_20220101_015A0250IW2_01.extension

---
**S3_01_SNT_03**

Data will be delivered using a partition of the territory in square tiles, obtained in projection **ETRS89-extended/LAEA Europe (EPSG: 3035**). Each tile will be 100km x 100km wide. The tile's upper left corner coordinates will name the tile. Given the width of the tile, the upper left coordinates will be the points having this form: (XX00000, YY00000), and the relative tile identifier will be EXXNYY. The tiling system, in particular, is the same used in EGMS.
Naming convention:
- Tile identifier - 6 chars (see example below)
- Component: 1 char ("V" or "E")
Example:
ISS Product Name: ISS_S301SNT03_20180101_20220101_E43N19V_01
ISS Product Name: ISS_S301SNT03_20180101_20220101_E43N19E_01

---
**S3_01_SNT_04**

In this case, a single dataset per track will be delivered.
Naming convention:
- Track + Orbit Direction - 4 chars (3 + "A"/"D")
Example:
ISS Product Name: ISS_S301SNT04_20180101_20220101_015A_01

---
**COSMO-SkyMed**

In this case, InSAR data generated per each of the considered CSK frames will be distributed employing a track-specific grid (i.e., once generated, the PS data is cropped using a grid aligned with the satellite track - see figures below).

A unique ID will identify each grid cell.

NOTE 1: PS/DS products will be cropped using the grid, not the input SAR data. The PS calculation will still be performed at the frame level.

NOTE 2: the case presented below constitutes an example of the grid we will generate and distribute. We will likely define a grid with larger cells.

<img src="images/csk_frame_grid.png" class="align-center" style="width:50.0%" alt="image" />
<img src="images/csk_frame_grid_zoom.png" class="align-center" style="width:50.0%" alt="image" />

---

# Metadata
### Sentinel-1

- **ISS_S301SNT01_2018-01-05_2022-12-28_08710208VVA_01.csv** - metadata file example for **S3-01-SNT-01** geospatial products.
- **ISS_S301SNT03_20180105_20221228_100ME43N18V_01.csv** - metadata file example for **S3-01-SNT-03** geospatial thematic products.



---

### CSK Areas Of Interest

| Area of Interest | ACR |
|-----------------------|-------|
| Milno_D | MIL_D |
| Milano_D | MIL_D |
| Firenze_A | FIR_A |
| Firenze_D | FIR_D |
| Venezia_A | VEN_A |
| Venezia_D | VEN_D |
| Pisa_A | PIS_A |
| Pisa_D | PIS_D |
| Andria_A | AND_A |
| Andria_D | AND_D |
| Etna_A | ETN_A |
| Etna_D | ETN_D |
| Volterra_A | VLE_A |
| Volterra_D | VLE_D |
| Matera_A | MAT_A |
| Matera_D | MAT_D |
| Vicenza_A | VIC_A |
| Vicenza_D | VIC_D |
| Siena_A | SIE_A |
| Siena_D | SIE_D |
| Treviglio_A | TRV_A |
| Treviglio_D | TRV_D |
| Ferrara_A | FER_A |
| Ferrara_D | FER_A |
| Mottola_A | MOT_A |
| Mottola_D | MOT_D |
| Ravenna_A | RVN_A |
| Ravenna_D | RVN_D |
| AbbadiaSanSalvatore_A | ASL_A |
| AbbadiaSanSalvatore_D | ASL_D |
| Verona_A | VER_A |
| Verona_D | VER_D |
| Torino_A | TOR_A |
| Torino_D | TOR_D |
| Aquileia_A | AQL_A |
| Aquileia_D | AQL_D |
| Chiavari_A | CHV_A |
| Chiavari_D | CHV_D |
| Modena_A | MOD_A |
| Modena_D | MOD_D |
| Urbino_A | URB_A |
| Urbino_D | URB_D |
| Colletorto_A | COL_A |
| Colletorto_D | COL_D |
| Cuneo_A | CUN_A |
| Cuneo_D | CUN_D |
| Sardegna | SRD_A |
| Sardegna | SRD_D |
| Belluno_A | BEL_A |
| Belluno_D | BEL_D |
| Assisi_A | ASS_A |
| Assisi_D | ASS_D |
| PiazzaArmerina | PAA_A |
| PiazzaArmerina | PAA_D |
| Crotone_A | CRO_A |
| Crotone_D | CRO_D |
| Tolmezzo_A | TOL_A |
| Tolmezzo_D | TOL_D |
| Noto_A | NTO_A |
| Noto_D | NTO_D |
| Bernina_A | BER_A |
| Bernina_D | BER_D |
| Celano_A | CLN_A |
| Celano_D | CLN_D |
| Genova_A | GEN_A |
| Genova_D | GEN_D |
| Bologna_A | BOL_A |
| Bologna_D | BOL_D |
| Pistoia_A | PST_A |
| Pistoia_D | PST_D |
| NoceraTerinese_A | NTR_A |
| NoceraTerinesa_D | NTR_D |
| Brennero_A | BRN_A |
| Brennero_D | BRN_D |
| Cortina_A | CRT_A |
| Cortina_D | CRT_D |
| Palermo_A | PAL_A |
| Palermo_D | PAL_D |
| Norcia_A | NRI_D |
| Norcia_D | NRI_D |
| Vulcano_A | VLA_A |
| Vulcano_D | VLA_D |
| Mattinata_A | MTI_A |
| Mattinata_D | MTI_D |
| ColliAlbani_A | COA_A |
| ColliAlbani_D | COA_D |

----

#### PYTHON DEPENDENCIES:
- [gdal: Python's GDAL binding.][]
- [fiona: Fiona is GDAL’s neat and nimble vector API for Python programmers.][]
- [numpy: The fundamental package for scientific computing with Python.][]
- [pandas: Python Data Analysis Library.][]
- [geopandas: add support for geographic data to pandas objects.][]
- [matplotlib: Library for creating static, animated, and interactive visualizations in Python.][]


[Language]: https://img.shields.io/badge/python-%3E%3D%203.10-blue
[License]: https://img.shields.io/bower/l/MI
[1]: ..%20image::%20https://www.python.org/

[xarray: Labelled multi-dimensional arrays in Python.]:https://docs.xarray.dev
[rasterio: access to geospatial raster data]:https://rasterio.readthedocs.io/en/latest/
[gdal: Python's GDAL binding.]: https://gdal.org/index.html
[tqdm: A Fast, Extensible Progress Bar for Python and CLI.]: https://github.com/tqdm/tqdm
[necdft4: Provides an object-oriented python interface to the netCDF version 4 library.]:https://pypi.org/project/netCDF4/
[fiona: Fiona is GDAL’s neat and nimble vector API for Python programmers.]:https://fiona.readthedocs.io/en/latest/
[numpy: The fundamental package for scientific computing with Python.]:https://numpy.org
[PyTMD: Python package for the analysis of tidal data.]: https://github.com/tsutterley/pyTMD
[pandas: Python Data Analysis Library.]:https://pandas.pydata.org/
[geopandas: add support for geographic data to pandas objects.]:https://geopandas.org/en/stable/
[matplotlib: Library for creating static, animated, and interactive visualizations in Python.]:https://matplotlib.org
17 changes: 7 additions & 10 deletions generate_grid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,18 +71,15 @@ def main() -> None:
args = parser.parse_args()

# - set path to input shapefile
# input_shapefile = args.input_file
input_shapefile \
= os.path.join('.', 'data', 'csk_frame_map_italy_epsg4326_dissolve',
'csk_frame_map_italy_epsg4326_dissolve.shp')

input_shapefile = args.input_file
#input_shapefile \
# = os.path.join('.', 'data', 'csk_frame_map_italy_epsg4326_dissolve',
# 'csk_frame_map_italy_epsg4326_dissolve.shp')
output_f_name \
= input_shapefile.split(os.sep)[-1].replace('dissolve.shp',
'atgrid.shp')

= os.path.basename(input_shapefile).replace('.shp','_grid.shp')
# - set path to output shapefile
#out_dir = args.out_dir
out_dir = os.path.join(r'C:\Users\e.ciraci\Desktop\test')
out_dir = args.out_dir
#out_dir = os.path.join(r'C:\Users\e.ciraci\Desktop\test')
os.makedirs(out_dir, exist_ok=True)

# - import input data
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