Drained wetlands can constitute a large source of greenhouse gas emissions, but the drainage networks in these wetlands are largely unmapped, and better maps are needed to aid in forest production and to better understand the climate consequences. We develop a method for detecting ditches in high resolution digital elevation models derived from LiDAR scans. Thresholding methods using digital terrain indices can be used to detect ditches. However, a single threshold generally does not capture the variability in the landscape, and generates many false positives and negatives. We hypothesise that, by combining the digital terrain indices using supervised learning, we can improve ditch detection at a landscape-scale. In addition to digital terrain indices, additional features are generated by transforming the data to include neighbouring cells for better ditch predictions. A Random Forests classifier is used to locate the ditches, and its probability output is processed to remove noise, and binarised to produce the final ditch prediction. The confidence interval for the Cohen's Kappa index ranges [0.655, 0.781] between the evaluation plots with a confidence level of 95%. The study demonstrates that combining information from a suite of digital terrain indices using machine learning provides an effective technique for automatic ditch detection at a landscape-scale, aiding in both practical forest management and in combatting climate change. © 2022 The Authors

Artificial Intelligence can make manufacturing organisations more effective and efficient, but it is not clear which AI tasks hold the greatest potential. Make-to-order manufacturers must constantly adapt to customers’ unique and rapidly changing needs, and therefore have different challenges than make-to-stock manufacturers. Our ambition is to develop an AI-enabled software system to support manufacturing organisations in improving their processes. To this end, we first seek to understand the data and technology requirements for key AI-enabled tasks in a make-to-order setting and determine the level of performance and explainability needed to address them. We perform a multiple case study of five make-to-order packaging manufacturers, interviewing personnel from sales, production, and supply chain to identify and prioritise operational challenges suitable for AI approaches. Demand forecasting emerges as the most important task, followed by predictive maintenance, quality inspection, complex decision risk estimation, and production planning. Participants emphasise the importance of explainable techniques to ensure trust in the systems. The results highlight a need for a greater control of the production process and a better understanding of customer needs. Although most of the tasks could be solved with current techniques, some, such as intermittent demand forecasting and complex decision risk estimation, would require further development. The study clarifies the potential of AI-enabled systems in make-to-order manufacturing and outlines the steps required to realise it.

Demand forecasting can optimise production and supply chain practices in manufacturing organisations. However, demand forecasting is not widely adopted among make-to-order (MTO) manufacturers with mass customisation offers. Building effective demand forecasting systems is challenging in such organisations due to the numerous unique manufactured articles and sparse demand patterns. This position paper argues that make-to-order manufacturers should employ demand forecasting to a larger extent, and that the forecasting community should address challenges related to the domain. Key challenges include creating models capable of predicting both demand size and timing of intermittent forecasts, as well as a deeper insight into the effects of global deep learning time-series models. We perform a pilot experiment using demand forecasting in a purchasing decision support system to validate the usefulness of demand forecasting for MTO manufacturing organisations with mass customisation offers. A research roadmap is proposed to address the identified challenges.