This essay assesses the morality of Ukraine's use of drones to attack targets inside Russia. Following its invasion by Russian forces, Ukraine has had a just cause to wage a war of self-defense. However, its efforts to achieve that cause remain subject to moral limits. Even a state that has been unjustly attacked may not, for example, respond by deliberately targeting the attacking state's civilian population. To do so would violate the jus in bello principle of discrimination. The essay first describes how drone technology has frequently enabled long-range strikes against Russian military assets as well as other targets inside cities. It then explains why it would be morally wrong for Ukraine to attack its enemy's population centers. First, Russian civilians are not liable to attack, and this nonliability is undiminished by the injustice of Russia's invasion or by any in bello wrongs committed by the Russian military. Second, attacking Russian cities with drones would arguably achieve little or no self-defensive benefit for Ukraine, and it could even be counterproductive.

The global increase in observed forest dieback, characterized by the death of tree foliage, heralds widespread decline in forest ecosystems. This degradation causes significant changes to ecosystem services and functions, including habitat provision and carbon sequestration, which can be difficult to detect using traditional monitoring techniques, highlighting the need for large-scale and high-frequency monitoring. Contemporary developments in the instruments and methods to gather and process data at large scales mean this monitoring is now possible. In particular, the advancement of low-cost drone technology and deep learning on consumer-level hardware provide new opportunities. Here, we use an approach based on deep learning and vegetation indices to assess crown dieback from RGB aerial data without the need for expensive instrumentation such as LiDAR. We use an iterative approach to match crown footprints predicted by deep learning with field-based inventory data from a Mediterranean ecosystem exhibiting drought-induced dieback, and compare expert field-based crown dieback estimation with vegetation index-based estimates. We obtain high overall segmentation accuracy (mAP: 0.519) without the need for additional technical development of the underlying Mask R-CNN model, underscoring the potential of these approaches for non-expert use and proving their applicability to real-world conservation. We also find that color-coordinate based estimates of dieback correlate well with expert field-based estimation. Substituting ground truth for Mask R-CNN model predictions showed negligible impact on dieback estimates, indicating robustness. Our findings demonstrate the potential of automated data collection and processing, including the application of deep learning, to improve the coverage, speed, and cost of forest dieback monitoring.

Aerial visuals play a central – and increasing – role in military operations, informing military decision-makers in real time. While adding relevant and time-sensitive information, these visuals construct an imperfect representation of people and spaces, placing additional burdens on decision-makers and creating a persuasive – yet misleading – virtual representation of the actual conditions on the ground. Based on interdisciplinary analysis of critical security studies, behavioural economics and international law literature, as well as rich data from US and Israeli military investigations into four military operations spanning from 2009 to 2021, this article identifies three types of challenges stemming from the mounting reliance on aerial visuals to inform military operations: technical challenges, relating to the technical capabilities and features of aerial vision technologies; cognitive challenges, relating to decision-making biases affecting human decision-makers; and human-technological challenges, relating to the human–machine interaction itself. The article suggests ways to mitigate these challenges, improve the application of the law of armed conflict, and protect people, animals and the environment during armed conflicts.

Vultures are among the most threatened bird guilds on the planet and have a unique functional role within ecosystems. They are therefore subject to increasing research interest, calling for standardised study approaches and monitoring methods. The use of Unmanned Aircraft Systems (UASs) is rapidly gaining popularity in ecological research due to technological advances, affordability, and accessibility. This study reviews the existing peer-reviewed publications and grey literature on the responses of European vultures and other comparable species to UASs, and summarises the types of UAS use, their potential disturbance effects on vultures, and the resulting inter- and intra-specific interactions. Our goal was to assess the potential effects of UASs and to provide practical recommendations to optimise their safe use in vulture conservation and research. We acknowledge the potential of UASs to increase research efficiency and reduce research effort, time, and financial cost. Owing to the absence of sufficient data on long-term disturbance effects, we advocate the precautionary principle and offer a set of species-tailored practical recommendations to limit the potential negative effects of UASs and maximise their value in conservation management. We urge that the physiological and long-term impacts on vulture reproduction are considered and call for standardised monitoring protocols and controls on UAS use. Our conclusions and recommendations are particularly aimed at researchers working on vulture conservation and restoration projects worldwide.

Coasts are undeniably regions of critical importance for a range of environmental, sociocultural, and economic reasons. Yet they are also areas of intense anthropogenic impact and are particularly susceptible to climate change-related concerns. As such, it is imperative that we have the means to monitor and manage them in a sustainable manner. Drone technology has emerged as providing a unique value proposition in coastal environments to support data-driven monitoring and management decisions. With their highly detailed data capture capability, drones are particularly well suited to map the spatial heterogeneity, structural complexity, and temporally dynamic nature of coasts. Further, they are readily accessible to coastal populations and can promote grassroots action by the very people whose lives and livelihoods the coasts support. Herein, we cover several of the leading innovations in using aerial drones to map coastal ecosystems. We then consider how general trends and technology projections including artificial intelligence, as well as cloud and edge computing offer opportunities for the future of drone mapping and monitoring in a coastal context. While the challenge of change is inevitable, embracing the opportunities it provides will allow us to better understand and live sustainably with and within our coastal ecosystems.

Endless armed conflicts against terrorist groups put civilian populations at risk. Since France has been involved in the Sahel from 2013 onwards, transnational non-international armed conflicts (NIACs) of extended geographical and temporal scope against groups designated as terrorists are not a US exception anymore. NIACs against terrorist groups, conducted not only by the United States but also by France, persist and have been reconfigured around threat anticipation. How can anticipatory warfare be best constrained? This article argues that it can be best done through more constraining rules regulating target selection in NIACs and, in particular, by redefining the notion of continuous combat function (CCF). Many elements explored in this article indicate that the United States and France select targets that they pre-designate as terrorists, before these targets are engaged in hostilities. Instead of responding to the observed participation of these individuals in hostilities, strikes are based on contextual and behavioural elements ahead or outside of such moments. This paper argues that when war consists of threat anticipation, it becomes very extensive and particularly risky for civilians. Furthermore, recent State practice in the counterterrorism context reveals the pitfalls of the notions of direct participation in hostilities and CCF as defined in the 2009 International Committee of the Red Cross Interpretive Guidance. Outside this context, the interpretations proposed in the Interpretive Guidance might seem sufficient to constrain target selection processes and to protect civilian populations. However, when applied to armed conflicts that are driven by threat anticipation, the pitfalls of these interpretations emerge. I formulate a critique of these interpretations as being partly responsible for anticipatory warfare and propose an alternative theory for the CCF test.

Drones represent a rapidly developing industry. Devices initially designed for military purposes have evolved into a new area with a plethora of commercial applications. One of the biggest hindrances in the commercial developments of drones is legal uncertainty concerning the legal regimes applicable to the multitude of issues that arises with this new technology. This is especially prevalent in situations concerning autonomous drones (ie drones operating without a pilot). This article provides an overview of some of these uncertainties. A scenario based on the fictitious but plausible event of an autonomous drone falling from the sky and injuring people on the ground is analysed from the perspectives of both German and English private law. This working scenario is used to illustrate the problem of legal uncertainty facing developers, and the article provides valuable knowledge by mapping real uncertainties that impede the development of autonomous drone technology alongside providing multidisciplinary insights from law as well as software electronic and computer engineering.