{"type":"script","doc_desc":{"producers":[{"name":"Reproducibility WBG","abbr":"DECDI","affiliation":"World Bank - Development Impact Department","role":"Verification and preparation of metadata"}],"prod_date":"2026-10-05","version":"1"},"project_desc":{"authoring_entity":[{"name":"Michael Schultz","affiliation":"World Bank","email":"schultz.consulting@outlook.com; mschultz@worldbank.org"},{"name":"Hao Li","affiliation":"National University of Singapore","email":"leebobgiser316@gmail.com"},{"name":"Himanshu Tiwari","affiliation":"German Aerospace Center (Deutsches Zentrum f\u00fcr Luft und Raumfahrt - DLR)","email":"ht.himanshu@gmail.com"},{"name":"Daniel Saslavsky","affiliation":"World Bank","email":"dsaslavsky@worldbank.org"},{"name":"Satya Prasad Sahu","affiliation":"World Bank","email":"ssahu8@worldbank.org"},{"name":"Daria Ulybina","affiliation":"World Bank","email":"dulybina@worldbank.org"}],"title_statement":{"title":"Reproducibility package for Reimagining Logistics and Connectivity from Space: Artificial Intelligence and Satellite Imagery Applications","idno":"RR_WLD_2025_378"},"data_statement":"Some data is restricted and has not been included in the reproducibility package. Commercial satellite imagery (PlanetScope, Pl\u00e9iades) and data derived from it, administrative truck-parking data from the Georgian Revenue Service, and confidential Aktau terminal throughput data cannot be redistributed. Training labels for Korea are withheld pending confirmation of their terms of use. Public data and processed Sentinel-1 derivatives are included. For more details, refer to the Datasets section and the README file.","software":[{"name":"Python","version":"3.14.6"},{"name":"ArcGIS Pro"}],"scripts":[{"title":"Reproducibility package for Reimagining Logistics and Connectivity from Space: Artificial Intelligence and Satellite Imagery Applications","date":"2026-09","notes":"Computational reproducibility verified by Development Impact (DECDI) Analytics team, World Bank.","instructions":"See README in reproducibility package.","file_name":"RR_WLD_2025_378","zip_package":"RR_WLD_2025_378.zip","dependencies":"R dependencies are listed in the file renv.lock. Python dependencies are described in the README."}],"repository_uri":[{"name":"Reproducible Research Repository (World Bank)","uri":"https:\/\/reproducibility.worldbank.org"}],"production_date":"2026-10-05","abstract":"This reproducibility package accompanies the World Bank report \"Reimagining Logistics and Connectivity from Space: Artificial Intelligence and Satellite Imagery Applications\". The report applies Earth Observation and Artificial Intelligence methods to monitor logistics infrastructure and activity. Applications include Sentinel-1 Synthetic Aperture Radar estimates of truck-park occupancy at Georgian International Road Transport (Transports Internationaux Routiers) parks and southern African border crossings, PlanetScope-based truck detection at Beitbridge, railcar estimates for the Chicago Clearing yard, port activity at Aktau, and an attribute-based Random Forest classification of warehouses from building footprints enriched with OpenStreetMap, WorldPop, and World Settlement Footprint attributes. The classifier is trained on data from Korea and transferred to Poland and Germany using Correlation Alignment (CORAL) domain adaptation.","geographic_units":[{"name":"World","code":"WLD"}],"topics":[{"id":"C80","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"General","parent_id":"C8"},{"id":"C88","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Other Computer Software","parent_id":"C8"},{"id":"O32","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Management of Technological Innovation and R&D","parent_id":"O3"},{"id":"Q56","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Environment and Development \u2022 Environment and Trade \u2022 Sustainability \u2022 Environmental Accounts and Accounting \u2022 Environmental Equity \u2022 Population Growth","parent_id":"Q5"}],"output":[{"type":"Wolrd Bank Report","description":"World Bank Report","title":"Reimagining Logistics and Connectivity from Space: Artificial Intelligence and Satellite Imagery Applications"}],"language":[{"name":"English","code":"EN"}],"disclaimer":"The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development\/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.","license":[{"name":"MIT License","uri":"https:\/\/opensource.org\/license\/mit"},{"name":"World Bank IGO Rider","uri":"https:\/\/github.com\/worldbank\/metadata-editor\/blob\/main\/WB-IGO-RIDER.md"}],"contacts":[{"name":"Satya Prasad Sahu","affiliation":"World Bank","email":"ssahu8@worldbank.org"},{"name":"Reproducibility WBG","affiliation":"World Bank","email":"reproducibility@worldbank.org"}],"datasets":[{"name":"Copernicus Sentinel-1 Ground Range Detected (GRD) SAR imagery","uri":"https:\/\/dataspace.copernicus.eu","access_type":"Data is publicly available and included in the reproducibility package.","note":"Raw Sentinel-1 scenes are not in the package; only project-processed derivatives (occupancy \/ queue-length estimates, variables Date; Occupancy) are included. Historical processing used the project's Sentinel Hub service and is not part of the package.\nObservations used: Figure 1, Figoil\/Sarpi TIR park (park 14), 77 observation pairs, 19 April 2023 to 19 February 2024; Figure 14, 18 Georgian TIR parks, 907 observation pairs over the same period; Figure 3, Lebombo\/Ressano Garcia border crossing, 445 observations, 3 January 2018 to 3 December 2023. Exact AOI polygons and full product IDs were not recovered.\nFiles location: Figure1_WBG_R_closeout_package\/data\/raw\/remote_sensing\/sentinel1_processed_sar_estimates\/14.xls (identical to Figure 14 ...\/14.xls); Figure14_WBG_R_closeout_package\/data\/processed\/worldbank_georgia\/1.xls to 26.xls (one per TIR park) and data\/raw\/kmls\/1.kml to 26.kml (TIR park boundary polygons, geometry only); Figure3_WBG_PR_closeout_package\/data\/raw\/prediction_ID_002_Lebombo_Sentinel1.zip (598 GeoTIFFs, 2016-2024, a broader set than the observations charted) and reference\/source_uploads\/prediction_ID_002_Lebombo_Sentinel1_results_timeSeries.zip (23 CSVs).\nNot included: Figure3_WBG_PR_closeout_package\/data\/report_source\/Figure3.xlsx, the workbook read by the Figure 3 code, because it also contains PlanetScope-derived detections and Crickmay GPS data; and Figure3_WBG_PR_closeout_package\/reference\/arcgis\/Figure3_SAR.ppkx (ArcGIS package with two Sentinel-1 rasters and Esri basemap references). Figure 3 therefore cannot be rerun from the public package.\nFigure 4: the original inputs were not part of the delivery, and Figure 4 is reconstructed from the published report (see the entry for the published report). A historical Figure4.xls (482 Date\/Occupancy observations, 2 March 2015 to 16 March 2024) was recovered later; its link to the published figure has not been verified and it is not included in the package.","citation":"European Space Agency (ESA), Copernicus Programme. Sentinel-1 Ground Range Detected (GRD) SAR imagery [dataset], processed by the World Bank project team. Copernicus Data Space Ecosystem. https:\/\/dataspace.copernicus.eu. Access date: 2024.","license":"Copernicus Sentinel Data Terms and Conditions","license_uri":"https:\/\/sentinels.copernicus.eu\/documents\/247904\/690755\/Sentinel_Data_Legal_Notice"},{"name":"Georgian Revenue Service Truck Parking Occupancy (TIR parks)","access_type":"Data access was granted directly to the study authors by the data owners\/managers. It was obtained under a custom data license that does not allow for redistribution and it is not included in the reproducibility package.","note":"Ground-truth truck counts for Georgian TIR parks, collected by the project from the Georgian Revenue Service TIR-park service. Variables: truck counts by date and time for TIR parks 1 to 26 and 1001; park list with Customs Checkpoint, TIR Park, Capacity, Shapefile, id, scrapper_id, scrapper_subid.\nFiles location: Figure1_WBG_R_closeout_package\/data\/raw\/ground_truth\/ ('ground truth TIR parks.xlsx', 'ground_truth_full_2min.csv', 'TIR Parks.csv'); Figure14_WBG_R_closeout_package\/data\/raw\/ (ground_truth_TIR_parks.xlsx, TIR_Parks.csv).\nThe data is not included in the package, so Figures 1 and 14 cannot be rerun from the public package. The live service cannot recreate the historical observations. Replicators can request the archived study extract and the site\/date mapping from the authors, who will coordinate with the Georgian Revenue Service; access is not guaranteed. See data_hash_report.csv (included in the reproducibility package) for the location and SHA-256 hash of each excluded file. For more information about the data, contact the authors at ssahu8@worldbank.org.","citation":"Georgian Revenue Service. Truck Parking Occupancy, TIR-park service [dataset]. https:\/\/www.rs.ge\/TirPark-en?cat=1&tab=1. Collected by the World Bank project team, 2023-2024. Unpublished data.","uri":"https:\/\/www.rs.ge\/TirPark-en?cat=1&tab=1"},{"name":"PlanetScope Analytic Surface Reflectance imagery","uri":"https:\/\/www.planet.com","access_type":"Data was obtained under a commercial license that does not allow for redistribution, and it is not included in the reproducibility package.","note":"Commercial imagery from Planet Labs PBC, and data derived from it, used for Figures 2, 12 and 13.\nFiles location: Figure2_WBG_PR_closeout_support\/ (560 *_3B_AnalyticMS_SR_harmonized_clip.tif scenes, Beitbridge); Figure2_WBG_PR_map_closeout_package and Figure2_WBG_PR_top_timeseries_closeout_package (derived detections, counts, annotations and scene manifest, including the processed files read by the Figure 2 code); Figure12_WBG_PR_closeout_package\/arcgis_ppkx\/; Figure13_WBG_report_correction_closeout_package\/data\/ (beta_v3 detector-score rasters, ground_truths and mask.png). The 42 beta_v3 composite rasters contain a single detector-score band and no reflectance bands.\nRaw imagery and derived files are not included, pending the applicable World Bank-Planet license terms. Figures 2, 12 and 13 therefore cannot be rerun from the public package. Replicators need their own Planet license; the scene list for Beitbridge (acquisitions from 15 December 2022 to 22 April 2023) is available from the authors. Original order IDs were not recovered. See data_hash_report.csv (included in the reproducibility package) for the location and SHA-256 hash of each excluded file. For more information about the data, contact the authors at ssahu8@worldbank.org.","citation":"Planet Labs PBC. PlanetScope Analytic Surface Reflectance imagery, Beitbridge [dataset]. Unpublished data. https:\/\/www.planet.com. Access date: 2024.","license":"Commercial License","license_uri":"https:\/\/www.planet.com\/pricing\/insights-platform\/"},{"name":"Pl\u00e9iades 1A\/1B very high-resolution imagery (Airbus Defence and Space, via SkyWatch)","uri":"https:\/\/skywatch.com\/satellite-imagery\/","access_type":"Data was obtained under a commercial license that does not allow for redistribution, and it is not included in the reproducibility package.","note":"Very high-resolution scene of Incheon used for Figure 6 and Table 8, obtained through SkyWatch (EarthCache).\nFiles location: Figure6_WBG_PR_closeout_VHR\/SKYWATCH_PHR_PS_20231120T0215_ALL_6bff.tif (~3 GB, a mosaic of four tiles: Tile_0_0, Tile_0_1, Tile_1_0, Tile_1_1) and its sidecar files (.tif.ovr, .tif.aux.xml, .tif.xml, .tfw).\nDerived data, also excluded: truck points manually digitized from the scene (detections_v2, truck_detection_20240325_v1) and the Incheon building tables that use them (truck_density attribute: Incheon_train_with_feature.geojson, Incheon_test_with_feature.geojson, Incheon_warehouse_prediction), in Figure6_WBG_PR_closeout_package_v2\/data\/ and Table8_WBG_PR_closeout_package_CLOSED\/data\/. Figure 6 and Table 8 therefore cannot be rerun from the public package.\nNo order ID was recovered. Replicators can request licensed access from SkyWatch using the scene name above. See data_hash_report.csv (included in the reproducibility package) for the location and SHA-256 hash of each excluded file. For more information about the data, contact the authors at ssahu8@worldbank.org.","citation":"Airbus Defence and Space. Pl\u00e9iades very high-resolution imagery, Incheon [dataset]. Distributed by SkyWatch. Scene SKYWATCH_PHR_PS_20231120T0215_ALL_6bff. Access date: 2024.","license":"Commercial License","license_uri":"https:\/\/skywatch.com\/contact-sales\/"},{"name":"Logistics Warehouse Information, Korea (MOLIT National Logistics Information Center)","uri":"http:\/\/nlic.go.kr\/nlic\/WhsInfoWarehouseSch.action","access_type":"Data access was granted directly to the study authors by the data owners\/managers. The terms of use for redistribution have not been confirmed, and it is not included in the reproducibility package.","note":"Source of the warehouse_label training labels for Korea. The source file is 'Logistics warehouse information_230614.ko.en.xls' (snapshot of 14 June 2023), covering commercial warehouses larger than 1,000 square meters; 4,803 geocoded points were used for manual digitization of warehouse footprints.\nFiles location: Table3_WBG_PR_closeout_package\/data\/korea_all_buildings_with_feature_updated.geojson (11,727 buildings; identical copy in Table7_WBG_PR_closeout_package\/data\/). These files are not included until the terms of use are confirmed, so the Korea results in Tables 3 and 7 cannot be rerun from the public package. See data_hash_report.csv (included in the reproducibility package) for the location and SHA-256 hash of each excluded file. For more information about the data, contact the authors at ssahu8@worldbank.org.","license":"Custom license","citation":"Ministry of Land, Infrastructure and Transport (MOLIT), National Logistics Information Center. 2023. Logistics Warehouse Information [dataset]. http:\/\/nlic.go.kr\/nlic\/WhsInfoWarehouseSch.action. Snapshot of 14 June 2023."},{"name":"Aktau Terminal Monthly Throughput","access_type":"Data access was granted directly to the study authors by the data owners\/managers. It is confidential and it is not included in the reproducibility package.","note":"Monthly throughput data for a terminal of the Aktau seaport, Kazakhstan, provided to the World Bank in confidence in 2023.\nFiles location: Figure11_Aktau_confidential_closeout_package\/data\/raw\/AktauAfter12Months_other_CONFIDENTIAL_SOURCE.xlsx (Year, Month, Occupancy).\nDerived data, also excluded: Figure11_Aktau_closeout_package\/data\/processed\/aktau_2022_public_indexed_series.csv (SAR occupancy and tonnage indices, January 2022 = 100). This file is a required input for the Figure 11 code, so Figure 11 cannot be rerun from the public package. No public access route exists; requests can be addressed to the authors. See data_hash_report.csv (included in the reproducibility package) for the location and SHA-256 hash of each excluded file. For more information about the data, contact the authors at ssahu8@worldbank.org.","citation":"Aktau seaport terminal operator. 2023. Aktau Terminal Monthly Throughput [dataset]. Unpublished confidential data shared with the World Bank.","license":"Custom license"},{"name":"Microsoft Global ML Building Footprints","uri":"https:\/\/github.com\/microsoft\/GlobalMLBuildingFootprints","access_type":"Data is publicly available and included in the reproducibility package.","note":"Building polygons used for attribute-based warehouse classification. Shape attributes (Shape_Leng, Shape_Area, long_ax, corners, squareness, shape_index, fractal_dimension, circular_compactness, square_compactness, elongation, convexity, ERI) were computed by the authors; OpenStreetMap, WorldPop and WSF attributes were added (see those entries).\nFiles location: Figure7_WBG_PR_closeout_package\/data\/warsaw_warehouse_prediction.geojson (704 buildings); Table3_WBG_PR_closeout_package\/data\/DE_buildings_with_feature_updated.geojson (2,385) and lodz_buildings_w2w_with_feature.geojson (2,368); Table4_WBG_PR_closeout_package\/data\/{warsaw,lodz,katowice}_warehouse_prediction_bigger5000\/ (shapefiles; 704, 74 and 47 records). The Korea building tables are described in the Korea warehouse labels entry.\nNot included: Figure8_ArcGIS_PR_closeout_package\/evidence\/Figure8.ppkx and its thumbnail. The ArcGIS Pro project contains four local layers derived from the Warsaw building table (2,470 building polygons, their centroids, DBSCAN clusters and a kernel-density raster) and references Esri basemaps (OpenStreetMap vector tiles, World Topographic Map, World Hillshade). Figure 8 was produced manually in ArcGIS Pro.","citation":"Microsoft. Global ML Building Footprints [dataset]. https:\/\/github.com\/microsoft\/GlobalMLBuildingFootprints. Access date: 2024.","license":"Community Data License Agreement \u2013 Permissive \u2013 Version 2.0","license_uri":"https:\/\/cdla.dev\/permissive-2-0\/"},{"name":"OpenStreetMap","uri":"https:\/\/www.openstreetmap.org","access_type":"Data is publicly available and included in the reproducibility package.","note":"Used to compute building attributes: highway_dis, closest_highway_length, highway_clossness_500, railway_dis and industrial_landuse. The building tables also contain airport_dis and seaport_dis, whose source is not documented in the package.\nFiles location: attributes in the building tables listed under the Microsoft Global ML Building Footprints entry (Figure 7, Tables 3 and 4) and the Korea warehouse labels entry. Table5_OSM_datatypes_NA_closeout_package\/data\/table5_osm_datatypes_source.csv lists OSM data types only (no OSM extracts).","citation":"OpenStreetMap contributors. OpenStreetMap [dataset]. https:\/\/www.openstreetmap.org. Access date: 2024.","license":"Open Data Commons Open Database License (ODbL 1.0)","license_uri":"https:\/\/www.openstreetmap.org\/copyright"},{"name":"WorldPop population density (1 km, 2020)","uri":"https:\/\/www.worldpop.org","access_type":"Data is publicly available and included in the reproducibility package.","note":"Files location: Figure7_WBG_PR_closeout_package\/data\/warsaw_pd_2020_1km.tif (identical to the Table 4 copy); Table4_WBG_PR_closeout_package\/data\/ (warsaw_pd_2020_1km.tif, lodz_pd_2020_1km.tif, katowice_pd_2020_1km.tif, pol_pd_2020_1km.tif). Also the worldpop attribute in the building tables listed under the Microsoft Global ML Building Footprints entry.","citation":"WorldPop (School of Geography and Environmental Science, University of Southampton). Population density 2020, 1 km resolution [dataset]. https:\/\/www.worldpop.org. Access date: 2024.","license":"Creative Commons Attribution 4.0 International (CC BY 4.0)","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"name":"World Settlement Footprint (WSF) Evolution and WSF3D (German Aerospace Center, DLR)","uri":"https:\/\/geoservice.dlr.de\/web\/maps\/eoc:wsfevolution","access_type":"Data is publicly available and included in the reproducibility package.","note":"Files location: Figure7_WBG_PR_closeout_package\/data\/warsaw_WSF_EVO_3857.tif (clipped and reprojected WSF Evolution). WSF3D building height is in the wsf3d attribute of the building tables listed under the Microsoft Global ML Building Footprints entry; WSF Evolution is in the wsf_evo attribute of the Warsaw and Katowice building tables. In Figure 7, buildings are grouped by WSF settlement-appearance year; the groups describe currently mapped buildings, not historical warehouse inventories.","citation":"German Aerospace Center (DLR). World Settlement Footprint (WSF) Evolution and WSF3D [dataset]. https:\/\/geoservice.dlr.de. Access date: 2024.","license":"Creative Commons Attribution 4.0 International (CC BY 4.0)","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"name":"U.S. Surface Transportation Board (STB) Rail Service Data","uri":"https:\/\/www.stb.gov\/reports-data\/rail-service-data\/","access_type":"Data is publicly available and included in the reproducibility package.","note":"File locations: Figure10_Chicago_closeout_package\/data\/raw\/chicago_clearing_monthly_source_extract.xlsx (variables: Date; Satellite detector: number of railcars; Ground truth: Number of railcars; Error) and data\/processed\/chicago_clearing_monthly_series.csv (month, sar_estimated_railcars, ground_truth_railcars, relative_error_sar_denominator). Ground truth is the STB Chicago Clearing yard inventory; the satellite series is the project's SAR detector output.","citation":"U.S. Surface Transportation Board. Rail Service Data: Chicago Clearing seven-day average yard inventory [dataset]. https:\/\/www.stb.gov\/reports-data\/rail-service-data\/. Access date: 2024.","license":"Public Domain Information"},{"name":"Reimagining Logistics and Connectivity from Space","access_type":"Data is publicly available and included in the reproducibility package.","note":"Values transcribed or digitized from the published report; no third-party data.\nFiles location: Figure4_PR_reverse_engineered_closeout_package\/reference\/ (3 report-page crops; the code regenerates data\/figure4_digitized_from_report_page26.csv from them); Figure5_attribute_classification_NA_closeout_package\/data\/figure5_{nodes,edges,stages}.csv; Figure15_OHR_pipeline_closeout_package\/data\/figure15_pipeline_steps.csv; Figure16_DA_NA_final_nooverlap_closeout_package\/data\/figure16_{nodes,edges,panels}.csv; Table1_EO_AI_applications_NA_closeout_package\/data\/table1_applications_source.csv; Table2_trade_node_indicators_NA_closeout_package\/data\/table2_indicators_source.csv; Table5_OSM_datatypes_NA_closeout_package\/data\/table5_osm_datatypes_source.csv; Figure13_WBG_report_correction_closeout_package\/reference\/figure13_published_transcription.csv.\nFigure4_PR_reverse_engineered_closeout_package\/data\/figure4_laxmannath_candidate_aoi_not_original.geojson is a reconstructed area of interest, not the original.","citation":"Sahu, Saslavsky, and Schultz. 2024. Reimagining Logistics and Connectivity from Space. Washington, DC: World Bank.","license":"Creative Commons Attribution 3.0 IGO (CC BY 3.0 IGO)","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/igo\/"}],"keywords":[{"name":"Earth Observation"},{"name":"Satellite Imagery"},{"name":"Artificial Intelligence"},{"name":"Remote Sensing"},{"name":"Logistics"},{"name":"Trade Connectivity"}],"technology_requirements":"Run time: ~1 hour.","technology_environment":"Paper exhibits were reproduced on a computer with the following specifications:\n\u2022 OS: macOS\n\u2022 Processor: Apple M4 Pro\n\u2022 Memory available: 24 GB\n\u2022 Software version: Python 3.14.6","reproduction_instructions":"To reproduce the findings of this report, a new user should do the following:\n\n1. Obtain the data. Public data is included in the package. Restricted data must be requested from the data providers (see the Datasets section) and placed under the paths listed in data_hash_report.csv. Use the SHA-256 hashes in that file to confirm that the files match those used in the verification.\n2. Open `main` py and set the top-level directory of the package.\n4. Run `main` py. It runs the code for each exhibit folder and writes the tables and figures to that folder's outputs. The full run takes approximately 1 hour.\n\n**What can be rerun with the public package alone:** Figures 4, 7, and 10 and Table 4. Figure 4 is a reconstruction digitized from the published report, not a rerun from original Sentinel-1 inputs. All other data exhibits (Figures 1, 2, 3, 6, 11, 12, 13 and 14 and Tables 3, 7 and 8) require restricted data that is not in the package; the reproducibility verification of these exhibits used data provided directly by the authors.\n\n**Note:** Figure 8 was produced manually in ArcGIS Pro and is not generated by `main`; the ArcGIS project is not included as it contains confidential data. Figures 3 and 13 include manual edits. Tables 1, 2, and 5 and Figures 5, 9, 15, and 16 are conceptual or descriptive exhibits and do not require reproduction. All the outputs generated by the replicators are included in the package so users can compare against the published report."},"datacite":{"creators":[{"givenName":"Michael","familyName":"Schultz","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Hao","familyName":"Li","nameType":"Personal","affiliation":[{"name":"National University of Singapore","affiliationIdentifier":"https:\/\/ror.org\/01tgyzw49","affiliationIdentifierScheme":"ROR"}]},{"givenName":"Himanshu","familyName":"Tiwari","nameType":"Personal","affiliation":[{"name":"German Aerospace Center (Deutsches Zentrum f\u00fcr Luft und Raumfahrt - DLR)","affiliationIdentifier":"https:\/\/ror.org\/04bwf3e34","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Daniel","familyName":"Saslavsky","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Satya Prasad","familyName":"Sahu","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Daria","familyName":"Ulybina","nameType":"Personal","affiliation":[{"affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","name":"World Bank","schemeUri":"https:\/\/ror.org"}]}],"titles":[{"lang":"en","title":"Reproducibility package for Reimagining Logistics and Connectivity from Space: Artificial Intelligence and Satellite Imagery Applications"},{"title":"RR_WLD_2025_378","titleType":"Other"}],"publisher":"World Bank","publicationYear":"2026","types":{"resourceType":"Reproducibility package","resourceTypeGeneral":"Other"},"url":"https:\/\/reproducibility.worldbank.org\/index.php\/catalog\/study\/RR_WLD_2025_378","language":"en"},"tags":[{"tag":"DOI"},{"tag":"Open Code"},{"tag":"Restricted data"}],"schematype":"script"}