2024-10-04, 14:00–14:20, Theatre Hall (Conference Center Laxenburg)
Cocoa cultivation serves as a crucial source of income for countless farmers across pantropical regions. However, this agricultural practice often leads to deforestation in tropical forests. While previous studies have highlighted the expansion of cocoa farms, particularly in select African countries, there remains a significant gap in comprehensive data regarding the location of cocoa farms on a pantropical scale. To address this challenge, our study employs deep learning models trained on Sentinel-1 and Sentinel-2 satellite imagery, coupled with annotated reference datasets, to map cocoa farms across pantropical regions.
Our findings provide valuable insights for governments, cocoa companies, consumers, NGOs, and international organizations striving to mitigate the challenges associated with escalating deforestation linked to cocoa production. Of particular significance is the utility of this dataset in addressing the recent European Union Regulation mandating companies to refrain from importing commodity crops associated with deforestation. By providing a comprehensive understanding of cocoa farm distribution across pantropical regions, our research contributes to informed decision-making and sustainable practices in cocoa production and trade.
Open-Earth-Monitor Cyberinfrastructure (Grant agreement ID: 101059548)
Dr. Robert Masolele is a post-doctoral researcher renowned for his work at the intersection of artificial intelligence and remote sensing, particularly in the field of classifying land use changes with a specific emphasis on commodity crops. His innovative research has contributed significantly to our understanding of how agricultural expansion, particularly in the cultivation of commodity crops, impacts global landscapes.
Education and Early Career:
Dr. Masolele earned his Ph.D. in Remote sensing and Machine learning from Wageningen University, where his passion for harnessing cutting-edge technologies to address pressing environmental challenges first took root. His early career saw him working on various projects related to satellite imagery analysis and machine learning applications, laying the foundation for his expertise in the intricate field of land use classification.
Expertise in AI and Satellite Imagery:
Specializing in the fusion of artificial intelligence and high-resolution satellite imagery, Dr. Masolele has developed advanced models and algorithms that excel in classifying land use changes with high accuracy. His research focuses on monitoring the expansion of commodity crops such as cacao, oil palm, rubber, coffee, avocado, pasture, and soy, providing valuable insights into the environmental repercussions of large-scale agricultural practices.
Notable Contributions:
One of Dr. Masolele's most notable contributions includes the development of a novel convolutional neural network (CNN) architecture tailored to handle the complexities of commodity crop identification. His work has led to breakthroughs in mapping the spatial distribution of land use following deforestation, understanding the dynamics of deforestation, and quantifying the ecological impact of crop expansion.
Collaborations and Impact:
Dr. Masolele is a sought-after collaborator in interdisciplinary research endeavors, fostering partnerships between environmental scientists, ecologists, and computer scientists. His work has had a tangible impact on sustainable land management practices and has been influential in shaping conservation policies in regions vulnerable to agricultural encroachment.
Publications:
In recognition of his outstanding contributions, Dr. Robert Masolele's work has been published in leading academic journals, media outlets, and he frequently presents his findings at international conferences.
As Dr. Masolele continues to push the boundaries of knowledge in his field, his dedication to leveraging artificial intelligence and satellite imagery for the betterment of global ecosystems remains a beacon of inspiration for the scientific community.