🛰️ IoT & Earth Observation

The IoT & Earth Observation sub-area deals with leveraging Internet of Things (IoT) technologies and satellite-based Earth observation systems to gather and analyze data for environmental and financial purposes. This field involves deploying sensors and satellite imagery to monitor parameters like air quality, land use, and climate conditions. The objective is to provide accurate, real-time information that improves risk assessment, operational efficiency, and strategic investments in climate-related projects.

Device Management

Effective sensor and IoT device management is critical as the number of IoT devices is projected to exceed 30 billion by 2025. This encompasses overseeing the deployment, calibration, and maintenance of sensors and IoT devices used for environmental monitoring and data collection. Core skills include ensuring the accurate operation of devices, managing data transmission, and troubleshooting technical issues.

Satellite Data Analysis

Satellite data analysis is vital for monitoring environmental conditions, with Earth-observing satellites providing over 2.5 million images of the Earth's surface daily. This cluster involves interpreting data captured by these satellites to assess land use, track changes in vegetation, and detect atmospheric phenomena. Competencies include remote sensing techniques, image processing, and spatial data analysis.

Remote Sensing

Integrating remote sensing technologies with IoT devices enhances environmental monitoring and management. This cluster incorporates satellite imagery, aerial data, and ground-based IoT sensors to collect comprehensive information on environmental conditions and resource usage. Green skills include expertise in remote sensing technology, IoT system integration, and data analytics.

Predictive Analytics

Data processing and predictive analytics play a crucial role in managing the vast amounts of data collected from IoT sensors and Earth observation systems. This includes processing data from satellites, sensors, and other monitoring technologies, applying machine learning algorithms, and developing predictive models to anticipate changes such as weather patterns, vegetation health, or natural disasters.