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          <span>Best Practices</span>
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          <p>Best Practices</p>
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        <table class="no-border">
          <tr>
            <th>Subject</th>
            <td>
              [Best Practice] Mapping Refugee Settlements by using
              PulseSatellite
            </td>
          </tr>
          <tr>
            <th>Name</th>
            <td>Secretariat</td>
          </tr>
          <tr>
            <th>Date</th>
            <td>2020-02-12</td>
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            <th>Views</th>
            <td>2,722</td>
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            <p class="note">
              <img src="./BP_1_files/202002121352284824.jpg" alt="BP_1: pic" />
              <span class="caption"
                >Figure 1. Refugee Camp mapping using PulseSatellite</span
              >
            </p>
            <p>
              Counting and classifying structures in a refugee settlement is a
              common analysis task for humanitarian agencies. In practice, this
              is currently manually done by human expert analysts using
              satellite imagery. A single settlement may have tens of thousands
              of structures, and identifying each of them can take several days.
              For camp mapping, PulseSatellite uses a Mask R-CNN model (He et
              al. 2017) trained on images from 12 settlements, where the image
              of each has been split into 300x300 pixel tiles and annotated by
              human experts. Once the model has been run on an unseen camp, the
              analyst can inspect the result in the tool and correct the outputs
              on a subset of tiles. An adaptation stage can then be performed to
              fine-tune the model to the unseen image and increase performance.
              In recent independent tests camp completion rates increased from
              77.3% to 94.7% after adaptation, with a final user accuracy of
              94.4% -in line with humanitarian performance requirements
              described in (Quinn et al. 2018).
            </p>
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              <img
                src="./BP_1_files/Uploadify_202002121349058190.jpg"
                alt="BP_1_2: pic "
              />
              <span class="caption">
                Figure2. PulseSatellite user-interface showing a hierarchy of
                learning models
              </span>
            </p>
            <p>
              PulseSatellite is a distributed cloud application, currently
              running on Amazon Web Services (AWS). The web-based user interface
              (UI) is written in JavaScript+HTML, and the web server, which
              communicates with the UI via HTTP and WS protocols, is written in
              NodeJS. The database is managed using PostgreSQL with PostGIS
              extension and is currently also used as a message streaming
              service for Pub/Sub messaging between the web, GPU and tiling
              servers. All backend files are generated and stored on a Amazon
              Elastic File System (EFS). Multiple GPU servers can run ML
              processes across a range of frameworks including PyTorch,
              TensorFlow and Keras. GIS processes, including the tiling servers,
              use OSGeo libraries and programs, most notably GDAL. All used
              software is open source.
            </p>
            <p class="note">
              * Note: The WG5 Best Practice "Mapping Refugee Settlements by
              using PulseSatellite" has extracted from a publication of
              <a href="https://arxiv.org/pdf/2001.10685" target="_blank"
                >Tomaz Logar et al. 2020</a
              >.
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