P08: Trees counting | |
Maturity score | |
Mean: 2.4 | STD: 0.70 |
Constraints and limitations · Cloud presence · Machine learning model uncertainty | |
Relevant user needs UN31: Need to link tree planting parcels to estimate the number of trees planted. | |
R&D gaps · Cost of Very High Resolution (VHR) satellite imagery which is essential for the product. · Global inconsistency due to the diversity of tree species. · Limitations in homogeneous forests where the trees are connected to each other. · The lack of local in-situ data to train and validate the models. · Lack of spectral resolution to differentiate between tree species | |
Potential improvements drivers · Advances in AI models to detect and count individual trees. · Datasets for training and validating the models. · Price models for commercial EO data. · Fusion of hyperspectral and multispectral EO data. | |
Utilisation level review | |
Utilisation score | |
Mean: 2.14 | STD: 0.64 |
No utilisation: Unawareness of the existence of this EO product Low utilisation · Higher cost of using the commercial EO product. · The current method (manually counting for a sample area and multiplying up to estimate the whole area) is considered good enough in terms of accuracy, reliability, and price. · Ground truth data is not sufficient for counting individual trees. Medium utilisation · Unawareness of the existence of the best available commercial EO product with better specifications. · Higher cost of using the best available commercial EO product . High utilisation | |
Critical gaps related to relevant user needs | |
Utilisation gap UN31: Need to link tree planting parcels to estimate the number of trees planted |
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