In 2023, 58.0% of the African population experienced moderate to severe food insecurity, with 21.6% facing severe food insecurity. Land-use and land-cover maps provide crucial insights for addressing food insecurity by improving agricultural efforts, including mapping and monitoring crop types and estimating yield. The development of global land-cover maps has been facilitated by the increasing availability of earth observation data and advancements in geospatial machine learning. However, these global maps exhibit lower accuracy and inconsistencies in Africa, partly due to the lack of representative training data. To address this issue, we propose a data-centric framework with a teacher-student model setup, which uses diverse data sources of satellite images and label examples to produce local land-cover maps. Our method trains a high-resolution teacher model on images with a resolution of 0.331 m/pixel and a low-resolution student model on publicly available images with a resolution of 10 m/pixel. The student model also utilizes the teacher model's output as its weak label examples through knowledge transfer. We evaluated our framework using Murang'a county in Kenya, renowned for its agricultural productivity, as a use case. Our local models achieved higher quality maps, with improvements of 0.14 in the F1 score and 0.21 in Intersection-over-Union, compared to the best global model. Our evaluation also revealed inconsistencies in existing global maps, with a maximum agreement rate of 0.30 among themselves. Our work provides valuable guidance to decision-makers for driving informed decisions to enhance food security.

This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes. Specifically, we aim to identify key objects -- Waterholes, Omuti homesteads, and Big trees -- around Oshikango in Namibia using sub-meter gray-scale aerial imagery from 1943 and 1972. In this work, we propose a workflow for analyzing historical aerial imagery using a deep semantic segmentation model on sparse hand-labels. To this end, we employ a number of strategies including class-weighting, pseudo-labeling and empirical p-value-based filtering to balance skewed and sparse representations of objects in the ground truth data. Results demonstrate the benefits of these different training strategies resulting in an average $F_1=0.661$ and $F_1=0.755$ over the three objects of interest for the 1943 and 1972 imagery, respectively. We also identified that the average size of Waterhole and Big trees increased while the average size of Omuti homesteads decreased between 1943 and 1972 reflecting some of the local effects of the massive post-Second World War economic, agricultural, demographic, and environmental changes. This work also highlights the untapped potential of historical aerial photographs in understanding long-term environmental changes beyond Namibia (and Africa). With the lack of adequate satellite technology in the past, archival aerial photography offers a great alternative to uncover decades-long environmental changes.

If the goal is to achieve better results in specific regions, models Cropland mapping can play a vital role in addressing environmental, that are tailored to those regions usually perform better than agricultural, and food security challenges. However, models that are designed for the whole world. in the context of Africa, practical applications are often hindered To this end, we develop a modeling workflow for generating by the limited availability of high-resolution cropland high-resolution cropland maps that are tailored toward a maps. Such maps typically require extensive human labeling, given area of interest (AOI), using Kenya as a use case. We use thereby creating a scalability bottleneck. To address this, we a deep learning based semantic segmentation workflow - an approach propose an approach that utilizes unsupervised object clustering often employed for land-cover maps [9, 10, 11, 12, 13]. to refine existing weak labels, such as those obtained In order to train the models, we used a mixture of sparse human from global cropland maps. The refined labels, in conjunction labels gathered in the AOI and weak labels from global with sparse human annotations, serve as training data for a cropland maps. Specifically we use the area of intersection semantic segmentation network designed to identify cropland between an unsupervised object based clustering of the input areas. We conduct experiments to demonstrate the benefits of satellite imagery and the weak labels to mine stronger cropland the improved weak labels generated by our method. In a scenario (positive class) and non-cropland (negative class) samples (see where we train our model with only 33 human-annotated Figure 1 for an overview of this approach).