Can Paris pledges avert severe climate change?

Current international climate negotiations seek to catalyze global emissions reductions through a system of nationally determined country-level emissions reduction targets that would be regularly updated. These "Intended Nationally Determined Contributions" (INDC) would constitute the core of mitigation commitments under any agreement struck at the upcoming Paris Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC). With INDCs now reported from more than 150 countries and covering around 90% of global emissions, we can begin to assess the role of this round of INDCs in facilitating or frustrating achievement of longer-term climate goals. In this context, it is important to understand what these INDCs collectively deliver in terms of two objectives. First, how much do they reduce the probability of the highest levels of global mean surface temperature change? Second, how much do they improve the odds of achieving the international goal of limiting temperature change to under 2-degrees C relative to preindustrial levels? Although much discussion has focused on the latter objective, the former is equally important when viewing climate mitigation from a risk-management perspective

Detecting plant species in the field with deep learning and drone technology:

Aerial drones are providing a new source of high‐resolution imagery for mapping of plant species of interest, amongst other applications. On‐board detection algorithms could open the door to allow for applications in which drones can intelligently interact with their environment. However, the majority of plant detection studies have focused on detection in post‐flight processed orthomosaics. Greater research into developing detection algorithms robust to real‐world variations in environmental conditions is necessary, such that they are suitable for deployment in the field under variable conditions. We outline the steps necessary to develop such a system, show by example how real‐world considerations can be addressed during model training and briefly illustrate the performance of our best performing model in the field when integrated with an aerial drone