Earth Observation Fundamentals for Resilience in the Caribbean Tender, Dominica - 98458845

Procurement Summary

Country : Dominica

Summary : Earth Observation Fundamentals for Resilience in the Caribbean

Deadline : 21 Mar 2024

Other Information

Notice Type : Tender

TOT Ref.No.: 98458845

Document Ref. No. : 0002007552

Competition : ICB

Financier : International Bank for Reconstruction and Development (IBRD)

Purchaser Ownership : Public

Tender Value : Refer Document

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Tender Details

Expression of Interest are invited for Earth Observation Fundamentals for Resilience in the Caribbean

ISSUE DATE AND TIME: Mar 07, 2024 20:36

CLOSING DATE AND TIME: Mar 21, 2024 22:59

PROJECT BACKGROUND: The Caribbean faces increasing frequency and severity of natural disasters; causing significant damage to infrastructure and hindering recovery efforts. Limited government capacity in utilizing geospatial data exacerbates challenges in decision-making for resilient infrastructure and land allocation post-disaster. There's a recognized need for improved data maintenance and training in advanced analytical models for better decision-making. This consultancy aims to address these issues by training government staff in GIS; remote sensing; and geospatial data science. This activity is part of the Digital Earth for a Resilient Caribbean Technical Assistance Project.2. OBJECTIVESThe firm will be responsible for designing and delivering a training program on the following topics:Remote Sensing (1.5 day training)Understand the applications of various types of remotely sensed data; based on the sensor; resolution; platform and other attributes.Learn how to access existing free global Earth Observation datasets including LANDSAT; MODIS and Sentinelperform basic analyses related to a problems or issues using freely available remote sensing data and applications; including: Perform analysis and visualization using QGIS.Data Science/Machine Learning (1.5 day training)Understand how Machine Learning can be used for feature analysis; identification; and classification. Understand how to use Python and Interactive notebooks to simplify and document processes and tools (such as this example) Perform basic analysis and visualisation of EO data using Python and Jupyter NotebooksApply Machine Learning models (in workshop practical sessions) developed by the World Bank / GFDRR team for the CaribbeanLiDAR (2 day training)Understand how LiDAR technology works and how it can be used to capture topographic and bathymetric information; including use cases and limitationsUnderstand how point clouds obtained from LiDAR and photogrammetry (e.g. drones) compare and how that relates to different usesUnderstand how to use common data products such as DTM; DSM; and contours to perform spatial analysis and support decision making with a focus on disaster risk reduction and risk assessmentUnderstand how LiDAR datasets can be made more accessible to different types of users

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