Special Issue on Large-Scale Deep Learning for Sensor-Driven Mapping

Jonathan Li; Ruisheng Wang; Roger Wheate

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Special Issue on Large-Scale Deep Learning for Sensor-Driven Mapping

机译：对传感器驱动映射大规模深度学习的特殊问题

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With rapid advances in sensing technologies, a huge amount of geospatial data can now be collected from sensors such as cameras, multi- and hyper-spectral scanners, synthetic aperture radar (SAR), and laser scanners. The sensing platforms include satellites, aircraft, unmanned aerial/ground vehicles, boats, trains, cars, and humans for backpack-carried or handheld sensors. The geometric and semantic information derived from such datasets are critical for making informed decisions and solving real world problems. However, how to accurately and reliably extract information from such datasets remains a challenging topic in cartography and other geoinformation communities.

机译：随着传感技术的快速进步，现在可以从摄像机，多谱和超光谱扫描仪，合成孔径雷达（SAR）和激光扫描仪等传感器收集大量地理空间数据。传感平台包括卫星，飞机，无人机/地面车辆，船只，火车，汽车和用于背包携带或手持式传感器的人。来自此类数据集的几何和语义信息对于提出明智的决策并解决现实世界问题至关重要。但是，如何准确和可靠地从这些数据集中提取信息仍然是制图和其他地理信息社区的具有挑战性的话题。

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来源
《Canadian Journal of Remote Sensing》 |2021年第3期|353-355|共3页
作者
Jonathan Li; Ruisheng Wang; Roger Wheate;
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作者单位

University of Waterloo ON Canada/ICA Commission on Sensor-driven Mapping Chair;

University of Calgary AB Canada/ICA Commission on Sensor-driven Mapping vice-chair;

University of Northern British Columbia BC Canada/ Canadian national delegate to the ICA;

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Special Issue on Large-Scale Deep Learning for Sensor-Driven Mapping

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