Fair Forward - Open Data & Use Cases

The Ghana Crop Disease Detection Challenge is an online data science challenge that seeks to leverage the power of Machine Learning and Artificial Intelligence to detect diseases that attack crops and offer immediate remedies.

the challenge was organized to predict biomass in shaded regions (cocoa plantations) in Cote d’Ivoire based on GEDI, Sentinel-2 and ground truth biomass data. Remote monitoring of the increase of biomass will help measure the impact of reforestation efforts in Cote d’Ivoire as well as detect degradation of forests due to cocoa, without requiring expensive and labor-intensive biomass estimates on the ground.

This speech dataset for the Ghanian languages Akan (Akuapem Twi, Asante Twi, Fante) and Ga includes 104,000 utterances (speech) across the four dialects/languages with approximately 200 speakers per dialect/language. This amounts to about 148 hours of speech in total. The dataset was developed to support the development of financial applications in native Ghanaian languages to allow illiterate and semi-literate people to fully benefit from digital financial services. Secondly, it aims to answer research questions related to domain-specific vs. general-purpose dataset development, dialects, as well as NLP system development in low resource settings.

This dataset supports yield estimation, crop type detection and classification, fruit detection and counting, and fruit maturity stage detection (unripe, ripe, and spoiled) for three products that are important sources of livelihood for millions of households in Sub-Saharan Africa. It contains 14,870 drone images with bounding box annotations of cashew, cocoa, and coffee trees collected across multiple farms in Ghana and Uganda. Conventional methods of yield estimation are expensive, require a lot of labor and time, and are prone to error due to incomplete ground observations. This results in poor crop yield estimations and hinders farmers’ ability to appropriately plan and manage their fields and production pipelines. This dataset will help transform African agriculture into agribusiness by allowing for the development of yield estimation solutions that enable farmers to make good business decisions. Having key details about agricultural production readily accessible enables a timely harvest, helping farmers ensure healthy, fresh produce and, in addition, better sales.

Empowering Women Across India with Voice-based Knowledge in Their Native Languages: By using openly accessible text-to-speech models from the Indian Institute of Science (IISc) and MeitY's Digital India Bhashini Division, Audiopedia created health-related 400+ audio messages that can be shared with civil society organizations for awareness raising and capacity building

This dataset is part of the initiative SYSPIN (SYnthesizing SPeech in INdian languages), that develops large open-source Text-to-Speech (TTS) corpora, i.e., speech training datasets and AI models in 9 Indian languages, namely – Bengali, Bhojpuri, Chhattisgarhi, Hindi, Kannada, Magahi, Maithili, Marathi, and Telugu. This data is useful for those wishing to build voice-enabled, user-friendly applications in Indian languages. Through voice-based solutions, these AI models can be used to empower communities that do not have access to digital services in their own native language. They can be used by private companies, researchers, NGOs or government departments to develop digital applications. The datasets and TTS models are available to the AI community and anyone wishing to build voice-enabled applications in Indian languages.

Improve the Kahi Ankahi Baatein (KAB) platform by fine-tuning Hindi LLMs for better user experience.

100 hours of Text to Speech Dataset for Mundari Language

This AI application and replication-kit is about an AI-based crop type map for Telangana. This should be of use to anyone wishing to support sustainable farming practices and evidence-based decision-making for agricultural. Also, this can be used to empower you, as a local business, and NGOs, or a government agency to develop digital agricultural applications. The application focuses on creating localized ground-truth data on several different crop related variables to train AI models tailored to Telangana's diverse agricultural landscape. This includes an open-access ground-truth dataset, a data challenge with openly available models for yield prediction, a crop classification model using satellite imagery applied to map the whole state of Telangana, and a roadmap for AI implementation in agriculture. Datasets, AI models, and crop maps are all openly available as an open-source replication kit and digital public good.

This AI application is about developing an AI-based system to tackle climate misinformation in Indonesia, focusing on creating accurate and accessible information tools. The initiative aims to build a multilingual disinformation detection system, covering Bahasa Indonesia and over 10,6 million words and 21 million words translation from Minangkabau, Balinese, and Buginese to classify and verify climate-related content. The application supports stakeholders, including government agencies, NGOs, and communities, in countering false narratives, promoting awareness, and enabling evidence-based decision-making. The AI outcomes include open-access datasets, disinformation, and topic classification models, a user-friendly website, and a chatbot. These tools empower users to identify climate misinformation, enhance public literacy, and foster collaboration for sustainable climate actions. The project emphasizes inclusivity, accessibility, and long-term impact, providing resources for researchers, local businesses, and policy-makers to advance AI applications in combating misinformation. Prosa has developed an AI-powered chatbot to address climate misinformation. The platform's website is available at faktaiklim.prosa.ai, and the chatbot can be accessed via Telegram (Fakta Iklim Bot), with open-source code available on GitHub. User guides are provided in four languages: Indonesian, Minangkabau, Balinese, and Bugis.

This project focuses on creating validated, open-source High Carbon Stock (HCS) forest maps for Indonesia using an AI-driven approach. It adopts the HCS classification system to identify forests suitable for conservation versus sustainable development: • High, Medium, and Low-Density Forests (Conservation Priority): Forests with closed or patchy canopies, rich biodiversity, and carbon storage. • Young Regenerating Forests: Regrowing forests that are transitioning into higher carbon stock potential. • Scrub and Open Land: Areas with minimal carbon stock suitable for sustainable development. The maps integrate field data, satellite imagery, and AI to assist policymakers, NGOs, and businesses in making data-driven decisions for sustainable land-use planning and conservation. Stakeholders can use the outputs for spatial planning, reducing greenhouse gas emissions, and implementing "no deforestation" commitments. The High Carbon Stock Approach (HCSA) project was nominated for the prestigious UN SDG Game Changer award – Category 2: Planet – Innovation for our climate and environment “Artificial Intelligence Meets Jungle/ Forest Forward.

By collecting more than 1000 hours of AI voice data (by 04/2025), this effort created the largest open-source voice dataset of diverse Swahili speakers for speech recognition (speech-to-text). You can use this resource to build AI systems understanding spoken Swahili e.g. in cellphones . This facilitates access to information in mother tongue, including by illiterate persons.

This team collected parallel text corpora for three Kenyan indigenous languages, Kidaw’ida, Kalenjin, and Dholuo, alongside Kiswahili, resulting in approximately 90,000 sentence pairs in total. After collection, the team separated out the Kidaw’ida, Kalenjin, and Dholuo sentences and used them as monolingual datasets for crowd-sourcing speech data, facilitated by uploading the sentences to Mozilla Common Voice. A total of 109 members of the three language communities were recruited to read and record sentences from their respective native languages. Emphasizing gender balance and including different ages and regional variants helped to make the datasets more representative. The voice datasets offer a substantial amount of speech data, comprising 56 hours of Kidaw’ida, 92 hours of Kalenjin, and 120 hours of Dholuo, for a total of 268 hours. Use cases for these parallel corpora include training models to translate text between Kiswahili and Kidaw’ida, Kalenjin, and Dholuo. The speech data on Mozilla Common Voice, along with its associated text data, is intended to be used for the development of speech recognition applications. The languages that comprise this dataset are low-resource, especially Kidaw’ida, which has only around 400,000 speakers and faces a more immediate risk of loss. By collecting the text and speech data, this team contributed to the preservation of these languages. They hope that once enough data has been collected to train accurate models and create NLP applications for these three languages, they will become more relevant in the modern digital age, thus mitigating the risk of loss.

This team annotated 2,125 Google Earth satellite images and 9,202 drone images, forming a combination of low and high-definition solar panel views in Madagascar. The Madagascar Initiatives for Digital Innovation (MAIDI) team performed field checks for up to 25% of satellite images and, in total, annotated 22,488 polygons. This dataset will help data scientists and users develop a solar panel detection algorithm to measure solar energy adoption across Madagascar. Notably, this project represented all regions of the country; instead of focusing only on big cities, it also covered average and small villages as well as coasts and mountains.

This dataset is the first large-scale human-annotated Twitter sentiment dataset for Hausa, Igbo, Nigerian-Pidgin, and Yorùbá, the four most widely spoken languages in Nigeria.

This effort created the largest open-source voice dataset of diverse Kinyarwanda speakers for speech recognition (speech-to-text). It collected more than 2300 hours of AI voice data in Kinyarwanda (by 04/2025). You can use this resource to build AI systems understanding spoken Kinyarwanda e.g. in cellphones . This facilitates access to information in mother tongue, including by illiterate persons.

Field data collected on agricultural farm land (crops) with raw datasets + applied use cases of classification of land use cover using ML (based on individual projects presented)

Univ of Stellenbosch Grant (Febe de Wet) - Voices of Mzansi project / localise SA local languages on Mozilla CV platform

This dataset contains annotated sentences with personally identifiable information (PII) in Luganda, Lumasaba, Hausa, and Kanuri. These four languages span Central and Eastern Uganda, Nigeria, Ghana, and Northern Cameroon. The team collected 3,000 sentences for both Kanuri and Hausa, 5,000 for Lumasaba, and 4,000 for Luganda. Potential use cases for these datasets include named entity recognition (NER), text classification, privacy-preserving data analysis and research, language modeling, machine translation, and linguistic research. The team aimed to curate a dataset that is gender inclusive, and their work highlighted the need for standardized guidelines for annotating low-resourced languages. Having these guidelines would help to avoid common pitfalls and errors when labeling text data in these low-resource languages.