Port berthing sites are regions of high interest for monitoring and optimizing port operations. Data sourced from the Automatic Identification System (AIS) can be superimposed on berths enabling their real-time monitoring and revealing long-term utilization patterns. Ultimately, insights from multiple berths can uncover bottlenecks, and lead to the optimization of the underlying supply chain of the port and beyond. However, publicly available documentation of port berths, even when available, is frequently incomplete - e.g. there may be missing berths or inaccuracies such as incorrect boundary boxes - necessitating a more robust, data-driven approach to port berth localization. In this context, we propose an unsupervised spatial modeling method that leverages AIS data clustering and hyperparameter optimization to identify berthing sites. Trained on one month of freely available AIS data and evaluated across ports of varying sizes, our models significantly outperform competing methods, achieving a mean Bhattacharyya distance of 0.85 when comparing Gaussian Mixture Models (GMMs) trained on separate data splits, compared to 13.56 for the best existing method. Qualitative comparison with satellite images and existing berth labels further supports the superiority of our method, revealing more precise berth boundaries and improved spatial resolution across diverse port environments.

Several methods have been proposed for correcting the elevation bias in digital elevation models (DEMs) for example, linear regression. Nowadays, supervised machine learning enables the modelling of complex relationships between variables, and has been deployed by researchers in a variety of fields. In the existing literature, several studies have adopted either machine learning or statistical approaches in the task of DEM correction. However, to our knowledge, none of these studies have compared the performance of both approaches, especially with regard to open-access global DEMs. Our previous work has already shown the potential of machine learning approaches, specifically gradient boosted decision trees (GBDTs) for DEM correction. In this study, we share some results from the comparison of three recent implementations of gradient boosted decision trees (XGBoost, LightGBM and CatBoost), versus multiple linear regression (MLR) for enhancing the vertical accuracy of 30 m Copernicus and AW3D global DEMs in Cape Town, South Africa.

The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artifacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30m DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The correction achieved significant accuracy gains which are competitive with other proposed methods. The root mean square error (RMSE) of Copernicus DEM improved by 46 to 53% while the RMSE of AW3D DEM improved by 72 to 73%. These results showcase the potential of gradient boosted trees for enhancing the quality of DEMs, and for improved hydrological modelling in urban catchments.

The interaction between elephants and their environment has profound implications for both ecology and conservation strategies. This study presents an analytical approach to decipher the intricate patterns of elephant movement in Sub-Saharan Africa, concentrating on key ecological drivers such as seasonal variations and rainfall patterns. Despite the complexities surrounding these influential factors, our analysis provides a holistic view of elephant migratory behavior in the context of the dynamic African landscape. Our comprehensive approach enables us to predict the potential impact of these ecological determinants on elephant migration, a critical step in establishing informed conservation strategies. This projection is particularly crucial given the impacts of global climate change on seasonal and rainfall patterns, which could substantially influence elephant movements in the future. The findings of our work aim to not only advance the understanding of movement ecology but also foster a sustainable coexistence of humans and elephants in Sub-Saharan Africa. By predicting potential elephant routes, our work can inform strategies to minimize human-elephant conflict, effectively manage land use, and enhance anti-poaching efforts. This research underscores the importance of integrating movement ecology and climatic variables for effective wildlife management and conservation planning.

Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustainable development of cities. Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs. In this paper, we present a deep reinforcement learning based approach to automatically layout roads for slums. We propose a generic graph model to capture the topological structure of a slum, and devise a novel graph neural network to select locations for the planned roads. Through masked policy optimization, our model can generate road plans that connect places in a slum at minimal construction costs. Extensive experiments on real-world slums in different countries verify the effectiveness of our model, which can significantly improve accessibility by 14.3% against existing baseline methods. Further investigations on transferring across different tasks demonstrate that our model can master road planning skills in simple scenarios and adapt them to much more complicated ones, indicating the potential of applying our model in real-world slum upgrading. The code and data are available at https://github.com/tsinghua-fib-lab/road-planning-for-slums.

If you are looking for an opportunity to work with data in a different and connected way, you might be the Data Engineer we are looking for! If you are an innovative Data Engineer, that likes connecting the dots in Data together and building high-performance Data Platforms that service multiple Data and Business Services, joining our team might just be the challenge you are looking for. You will be responsible for expanding and optimising our data and data pipeline architecture, as well as optimizing data flow and collection for cross-functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products.

Ozow is looking for a Lead AI/ML Engineer who will lead a team of AI/ML Engineers and Data Scientists to develop AI product to drive Ozow's business ambitions. As a Lead AI/ML Engineer at Ozow, you will create value through advanced data solutions that will elevate Ozow's ability to deliver trusted data for business decision-making. You will be a lead in a team of Data Scientists in developing highly, fit-for-purpose data solutions that incorporate insights to drive value-adding decision-making. We have a unique strategy for incorporating data governance, data architecture, and data operations components to automate the delivery of trusted data for data solutions. You will provide automation support for ML pipelines; build code, run tests (CI), and safely deploy a new version of an application (CD) to allow for the removal of manual errors, and provide standardised feedback loops, to enable fast product iterations.

A rare giant squid was discovered dead on a beach in Cape Town, South Africa, months after another washed up six miles away. Twitter user Tim Dee, who found the strange-looking sea creature on Scarborough Beach on Tuesday, shared photos and videos online that show the colorful squid's gigantic eye. 'Giant squid species wrecked on Scarborough beach this morning,' he wrote. Twitter user Tim Dee, who found the strange-looking sea creature (above) on Scarborough Beach on Tuesday, shared photos and videos online that show the colorful squid's gigantic eye Dee's video shows a marine biologist pulling back flesh to reveal the squid's huge beak that it uses for hunting and fishing. The sea creature, which looks like something Salvador Dali would have painted, is also known for having a very large eye - usually up to 11 inches in diameter with a 3.5 inch pupil.

Organized by Divine Fuh, HUMA – Institute for Humanities in Africa at the University of Cape Town, South Africa and Fanny Chabrol, CEPED-IRD, France and funded by Carnegie Corporation of New York and the Wenner-Gren Foundation, this workshop is located within the framework of the project Future Hospitals: 4IR/AI and the Ethics of Care at HUMA – Institute for Humanities in Africa headed by Divine Fuh, and the "Hospital Multiple" at CEPED-IRD headed by Fanny Chabrol. The workshop aims at proposing new ethnographic methodological and conceptual tools to think and imagine the "hospital of the future" in Africa, in particular, the way artificial intelligence (AI) seeks to transform and is currently transforming access to health care in hospitals today and in the coming years. Our project aims to build a problematisation of the hospital of the future and an ethnographic method to critically analyse the ethical, regulatory, and political issues with respect to AI, healthcare, and hospitals on the continent. We consider the "hospital of the future" – through the digitalization and computer automation of healthcare – as a global promise that needs to be challenged by ethnographic methods within hospitals, engaging with persons interacting with them. The first line of inquiry will challenge the logic of adoption and Africa as a place where development policies are implemented, where infrastructure projects are developed, in which technological innovation, mainly coming from the West, is presented as the promise of better health for those in need.

IndyGeneUS AI (pronounced "indigenous", a South Africa Founder Institute portfolio company) opens its seed round with a $1.5M investment from South African based venture capital company IsimoVest Venture Capital Partners in addition to an undisclosed amount from a South African angel investor. Located in Cape Town, IsimoVest is a Pan-African venture capital company that specializes in early stage, high-potential technology ventures that will likely provide highly impactful corporate social investment solutions. As a cutting-edge investment firm, IsimoVest is dedicated to economically empowering communities for the benefit of Africans. To that end, the firm will support IndyGeneUS AI in establishing its sequencing facility in Cape Town and establishing local key strategic partnerships. IndyGeneUS AI is developing a Multiomic data analysis and management platform that can detect new signature sequences such as biomarkers or polygenic risk scores by integrating "omics" data, meta data and textual information such as Electronic Healthcare Record data.