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Language documentation is increasingly seen as a collaborative process, engaging community members as active participants. Collaborative research produces better documentation that is valuable for both the academic community and the speakers. However, in many communities, speakers and language advocates lack the skills necessary to fully engage in collaborative projects. One way to overcome this barrier is to provide language documentation training to community members. Such training should teach participants how to ethically and comprehensively complete every stage of the documentation process while offering opportunity for theoretical discussion and practical application. In this paper, we offer one possible model for community-based training in language documentation and conservation that focuses on bidirectional learning and capacity building. We describe a training workshop that was held in 2018 in Kupang, the capital of Indonesia’s Nusa Tenggara Timur (NTT) province. A collaboration between the University of Hawai‘i, Leiden University, and Artha Wacana Christian University, this workshop implemented a model based on the practices of the Language Documentation Training Center (LDTC), an organization devoted to training speakers to document their own languages. We detail the NTT workshop itself, summarize post-workshop feedback, and offer suggestions to others looking to provide similar training in speaker communities. 

Land Use and Land Cover Changes (LULCC) are occurring rapidly around the globe, particularly in developing island nations. We use the lens of the United Nations’ Sustainable Development Goals (SDG) to determine potential policies to address LULCC due to increasing population, suburbia, and rubber plantations in Semarang, Indonesia between 2006 and 2015. Using remote sensing, overlay analysis, optimized hot spot analysis, expert validation, and Continuous Change Detection and Classification, we found that there was a spread of urban landscapes towards the southern and western portions of Semarang that had previously been occupied by forests, plantations, agriculture, and aquaculture. We also witnessed a transition in farming from agriculture to rubber plantations, a cash crop. The implications of this study show that these geospatial analyses and big data can be used to characterize the SDGs, the complex interplay of these goals, and potentially alleviate some of the conflicts between disparate SDGs. We recommend certain policies that can assist in preserving the terrestrial ecosystem of Semarang (SDG 15) while creating a sustainable city (SDG 11, SDG 9) and providing sufficient work for individuals (SDG 1) in a growing economy (SDG 8) while simultaneously maintaining a sufficient food supply (SDG 2).