Philippines GIS data

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of February 09, 2026, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of November 11, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of November 06, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of August 14, 2026, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of August 26, 2026, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of August 19, 2026, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of August 30, 2026, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of August 31, 2026, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of September 03, 2026, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of September 07, 2026, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

**UNOSAT code: TC20201111PHL** This map illustrates satellite-detected surface waters in CAR and Cagayan Valley regions, Philippines as observed from a Sentinel-1 image acquired on 13 November 2020 at 17:58 local time. Within the analyzed area of about 18,000 km2, a total of about 970 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 370,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: EQ20220727PHL** This map illustrates potentially damaged buildings and damaged buildings in La Paz Town, Abra Province, Cordillera Administrative Region, Philippines as detected by Pléiades satellite image acquired on 29 July 2022. Within the analyzed area, UNOSAT has identified 8 potentially damaged buildings. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: EQ20220727PHL** This map illustrates potentially damaged buildings and damaged buildings in Bangued Town, Abra Province, Cordillera Administrative Region, Philippines as detected by Pléiades satellite image acquired on 29 July 2022. Within the analyzed area, UNOSAT has identified 137 potentially damaged buildings and 10 damaged buildings. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: TC20201101PHL** This map illustrates floodwater depth in Camarines Sur provinces of Philippines based on surface waters observed from a Sentinel-1 image acquired on 1st of November 2020 and a digital elevation model data with the floodwater depth estimation tool(FwDET). This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

# EOS-RS Flood Proxy Map: Ilocos Norte, Philippines, Typhoon Krathon Floods, 4 Oct 2024, v0.9 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Flood Proxy Map (FPM) depicting areas that are likely flooded in Ilocos Norte, Philippines, due to heavy rainfall from Typhoon Krathon. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (13 Nov 2023 to 29 Apr 2024) and after (2 Oct 2024) the event. The map covers an area indicated by the white polygon. The preliminary map shows areas which are likely flooded (shown by light blue pixels of approximately 30m in size) due to the heavy rainfall from Typhoon Krathon. Preliminary validation was conducted using news reports and ground-level videos and imagery in selected areas. This map should be used as a guide to identify areas that are likely flooded, and may be less reliable over urban and vegetated areas. This product contains modified Copernicus Sentinel data (2023-2024), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2023-2024)

# EOS-RS Flood Proxy Map: Bicol Region, Philippines, Tropical Storm Trami Floods, 29 Oct 2024, v0.9 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Flood Proxy Map (FPM) depicting areas that are likely flooded in the Bicol Region, Philippines, due to heavy rainfall from Tropical Storm Trami. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (8 Feb 2024 to 5 Oct 2024) and after (29 Oct 2024) the event. The map covers an area indicated by the white polygon. The preliminary map shows areas which are likely flooded (shown by light blue pixels of approximately 30m in size) due to the heavy rainfall from Tropical Storm Trami. Preliminary validation was conducted using news reports, aerial imagery, and ground-level imagery and videos in selected areas. This map should be used as a guide to identify areas that are likely flooded, and may be less reliable over urban and vegetated areas. This product contains modified Copernicus Sentinel data (2024), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2024)

**ADAM ID: **sm\_us6000u0x4 Magnitude 5.5 earthquake at 52.054 depth occurred on Oct 08 2026 25km S of Glan. The epicentre of the earthquake was located at latitude 5.6006 longitude 125.1521. It impacted 248142 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

**UNOSAT code: TC20211216PHL** This map illustrates potentially damaged structures and buildings in City of Carcar and San Fernando municipalities, Cebu province, Region VII (Central Visayas), Philippines as observed from a Pleiades image acquired on 23 December 2021 & a GeoEye-1 image acquired on 20 December 2021. Within the analyzed area, UNOSAT identified 1929 damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

**UNOSAT code: TC20211216PHL** This map illustrates potentially damaged structures and buildings in ubay municipality, Bohol province, Region VII (Central Visayas), Philippines as observed from a Pleiades image acquired on 23 December 2021. Within the analyzed area, UNOSAT identified 1,751 damaged structures and 220 potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

**UNOSAT code: TC20211216PHL** This map illustrates potentially damaged structures and buildings in City of Talisay, Cebu province, Region VII (Central Visayas), Philippines as observed from a GeoEye-1 image acquired on 20 December 2021 & a Pleiades image acquired on 21 December 2021. Within the analyzed area, UNOSAT identified 483 damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

**UNOSAT code: TC20211216PHL** This map illustrates potentially damaged structures and buildings in Bien Unido, Trinidad and Ubay municipalities, Bohol province, Region VII (Central Visayas), Philippines as observed from a Pleiades image acquired on 23 December 2021. Within the analyzed area, UNOSAT identified 2,225 damaged structures and 888 potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

**ADAM ID: **sm\_us6000tif5 Magnitude 5.9 earthquake at 10.0 depth occurred on Aug 05 2026 31km SW of Sarangani. The epicentre of the earthquake was located at latitude 5.1757 longitude 125.2809. It impacted 33476 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us6000ti6x Magnitude 6.3 earthquake at 10.0 depth occurred on Aug 05 2026 30km SW of Sarangani. The epicentre of the earthquake was located at latitude 5.1812 longitude 125.276. It impacted 33475 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us7000ss6g Magnitude 5.4 earthquake at 72.962 depth occurred on Jun 11 2026 60km S of Sarangani. The epicentre of the earthquake was located at latitude 4.8685 longitude 125.4495. It impacted 2 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us6000rgkr Magnitude 6.0 earthquake at 59.326 depth occurred on Oct 11 2025 11km E of Cagwait. The epicentre of the earthquake was located at latitude 8.92 longitude 126.3963. It impacted 147451 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of May 21, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

**ADAM ID: **sm\_us6000rfx2 Magnitude 5.9 earthquake at 61.636 depth occurred on Oct 10 2025 41km E of Manay. The epicentre of the earthquake was located at latitude 7.1014 longitude 126.8879. It impacted 61234 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us7000ss57 Magnitude 5.5 earthquake at 49.891 depth occurred on Jun 11 2026 59km S of Sarangani. The epicentre of the earthquake was located at latitude 4.8778 longitude 125.4356. It impacted 2 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us6000rfwz Magnitude 7.4 earthquake at 58.087 depth occurred on Oct 10 2025 22km E of Caraga. The epicentre of the earthquake was located at latitude 7.2649 longitude 126.7545. It impacted 116915 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us6000rgj4 Magnitude 5.5 earthquake at 60.094 depth occurred on Oct 11 2025 27km E of Caraga. The epicentre of the earthquake was located at latitude 7.2713 longitude 126.798. It impacted 114933 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of July 06, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.