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Workshop on Complex Data Challenges in Earth Observation 2021

November 1 1:00 pm 7:00 pm SAST

The first Workshop on Complex Data Challenges in Earth Observation will be held as a satellite event at the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), a virtual conference.

The workshop focuses on advancing research in Earth observation by interpreting the high-dimensional heterogeneous data obtained by high-resolution remote sensing technologies.

Big data accumulated by ground, aerial, and satellite-based remote sensors at an unprecedented scale and resolution invite the application of modern data-hungry machine learning (ML) methods.

The workshop aims to bring together researchers in the fields of remote sensing, geographic information systems, weather and climate modelling, computer vision, and others with a general interest in applying data-driven models in EO.

The workshop invites advanced applications and method development in image and signal processing, data fusion, feature extraction, meta learning, and many more.

The workshop topics include:

  • spatiotemporal data processing and analysis;
  • multi-resolution, multi-temporal, multi-sensor, and multi-modal data fusion;
  • ML for weather and climate research;
  • deep learning and its applications to e.g., semantic segmentation, scene classification, and feature extraction;
  • advanced applications of time-series data analysis, e.g., urban sprawl, deforestation, crop monitoring, weather forecasts;
  • feature extraction, feature selection, and dimensionality reduction;
  • meta learning, including transfer learning, few-shot learning, and active learning;
  • data acquisition and efficient pre-processing of diverse remote sensing measurements including:
  • passive sensor images (panchromatic, multispectral, and hyperspectral);
  • active sensor data (LiDAR, RADAR, and SAR);
  • integration and aggregation of complementary remote sensing measurements;
  • advances in signal processing with applications to, e.g., unmixing, denoising;
  • benchmark datasets with application to EO.

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