intelligent

Aiforgood Asia ’ s ESG-as-a-Service helps match local NGOs and community leaders with corporations looking to run AI-related ESG projects , leveraging AI and remote sensing technologies to improve health and welfare , reduce inequality , fight climate change and preserve our oceans and forests .

To address the challenge in Mu Cang Chai forest , Aiforgood Asia partnered with Crayon , an IT service specialist with expertise in data and AI , to develop a Proof-of-Concept ( PoC ) computer vision-based solution that utilised remote sensing and Machine Learning ( ML ) to detect cardamom crops in satellite imagery . The PoC was tested in the northern ‘ core ’ region of the conservation area , with the outcomes evaluated based on feasibility and cost .

The results of the PoC showed that remote sensing and ML could , in principle , effectively detect cardamom crops in satellite imagery , providing that the quality of the satellite image was sufficient . This is the first step towards providing Fauna & Flora with a platform that can be scaled to support their conservation activities in the Mu Cang Chai forest and across Vietnam . The study also revealed that the image resolution necessary for detecting cardamom and acquiring ground-truthing data was an essential factor in the cost of the project .

The team faced a significant challenge when they began their project : how to source the most cost-efficient remote sensing data for their model . Their ideal source was Sentinel-2 , a satellite constellation launched by the European Space Agency . Images from Sentinel-2 are free to access and have a regular frequency , providing more opportunities to obtain a cloud-free image .

However , it became clear that cardamom production was not visible in low- and medium-resolution satellite images , including Sentinel-2 and Landsat . To gather the necessary ground truth data for training their model , the Crayon team had to rely on experts to gather GPS locations of cardamom crops during field expeditions .

Fortunately , the Fauna & Flora team was able to provide ground truth data for the project with the help of a grant from the Darwin Initiative . They used geo-referenced and orthomosaiced drone images gathered during their ground truth expeditions to accurately label the cardamom areas . Data was processed in DroneDeploy software , as part of a partnership with Fauna & Flora .

To build the model , the team used a U-Net architecture , a widely used classification architecture . They found that the colour gradient within the cardamom area was smaller than in other forested areas due to the plants ’ size and homogeneous canopy .

The model was trained on a 500x500m 2 area with approximately 96 % pixel within-sample accuracy . The team used high-resolution satellite imagery from WorldView-2 , which has a resolution of 0.46 meters , to train their model .

To obtain the data , they purchased small areas from EarthImages to keep costs low . The team also used QGIS software for parts of the exploratory data analysis and for sharpening the multi-spectral bands of the high-resolution image with pan-optical data , which resulted in an approximately 4x higher resolution .

Lam Van Hoang , Country Director at Fauna & Flora International , Vietnam , and Hattie Branson , Technical Specialist at Fauna & Flora International , spoke exclusively to Intelligent SME . tech about the project :

How central is technology for preserving life on earth ?

Van Hoang : I consider technology very central in carrying out conservation all over the world , not just Vietnam . It can inform not only the conservation actors , but also the government offices , partners and the local communities . All over the world , technology needs to be at the centre of conservation .

Previous page : Lam Van Hoang , Country Director at Fauna & Flora International Below : credit – Fan Pengfei / Dali University / Fauna & Flora

ALL OVER THE WORLD , TECHNOLOGY NEEDS TO BE AT THE CENTRE OF CONSERVATION .