Fish Stock Assessment

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Product Category

• Land use
• Land cover
• Natural Disaster
• Climate Change
• Coast Management 

• Marine
• Earth's Surface Motion

Financial Domains

• Investment management 
• Risk Analysis 
• Insurance management 
• Green finance

User requirements 

UN37: Projection of risk to portfolio assets into future

Description

VHR satellite imagery can assess the condition of solar panels, detect anomalies or defects, and evaluate the overall energy generation of the installation. By comparing historical data, it becomes possible to identify changes in performance over time and address maintenance or operational issues promptly. In addition, satellite imagery can be used to evaluate land use changes that affect the performance of solar panels such as shadows from tall buildings and vegetation cover.

Spatial Coverage Target

Asset level

Data Throughput

Rapid tasking 

Data availability

• High
• High

• Low
• Low

PRODUCT SPECIFICATIONS

Main processing steps

After the acquisition and preprocessing of the optical VHR (< 0.5 m), deep learning-based object detection algorithms such as YOLO can be used to detect solar panels. First, the dataset of the VHR images would be divided into training, validation, and test datasets. Training and validation will be used to train and optimize the deep learning model, which would be used then for inference to detect solar panels in the test data (our interest). Subsequently, temporal image pairing and image registration would be applied to analyse changes in the solar panels. Then, change detection techniques should be applied to the detected solar panels to identify changes over time. In terms of monitoring vegetation cover over the solar panels, vegetation indices can be used with change detection techniques.

Input data sources

Optical:  VHR based on the availability like Pleiades 1A/1B & NEO, WorldView2&3, and SPOT6/7

Radar:  N.A

Supporting data:   Solar panel datasets for deep learning models (if any)

Accessibility

VHR imagery: commercially available on demand from EO service providers.

Spatial resolution

Optical VHR: ≤ 0.5 m

Frequency (Temporal resolution)

Optical VHR: Daily

Latency

Daily

Geographical scale coverage

Globally

Delivery/ output format

Data type: Raster

File format: GeoTIFF

Accuracies

Thematic accuracy: 80-90%

Spatial accuracy: 1.5-2 pixels of input data

Constraints and limitations

• Cloud presence.
• The availability and size of solar panels dataset to train the deep learning model.
• Cost as balancing higher spatial resolution (to detect small panels) with broader coverage (to monitor larger installations) can be challenging due to cost constraints.
• The exact timing of solar panel installations might be a challenge due to cost constraints.
• Panels integrated into complex rooftop configurations can be harder to identify due to varying angles and orientations.

User's level of knowledge and skills to extract information and perform further analysis on the EO products.

Skills: Essential

Knowledge: Essential