Effective management of agricultural landscapes is critical for meeting global biodiversity targets, but efforts are hampered by the absence of detailed, large-scale ecological maps. To address this, we introduce Farmscapes, the first large-scale (covering most of England), high-resolution (25cm) map of rural landscape features, including ecologically vital elements like hedgerows, woodlands, and stone walls. This map was generated using a deep learning segmentation model trained on a novel, dataset of 942 manually annotated tiles derived from aerial imagery. Our model accurately identifies key habitats, achieving high f1-scores for woodland (96\%) and farmed land (95\%), and demonstrates strong capability in segmenting linear features, with an F1-score of 72\% for hedgerows. By releasing the England-wide map on Google Earth Engine, we provide a powerful, open-access tool for ecologists and policymakers. This work enables data-driven planning for habitat restoration, supports the monitoring of initiatives like the EU Biodiversity Strategy, and lays the foundation for advanced analysis of landscape connectivity.

Britain's trees are evolving resistance to the deadly ash dieback fungus, scientists have revealed. The disease, which arrived in Britain in 2012, has wrought havoc on the countryside, leaving behind the skeletal remains of dying ash trees. Previous estimates predict that up to 85 per cent of ash trees will succumb to the disease, and COBRA have held emergency meetings about how to deal with the issue. But now, experts have discovered that a new generation of ash trees, growing naturally in woodland, exhibit greater resistance to the disease compared to older trees. They found that natural selection is acting upon thousands of locations within ash tree DNA, driving the evolution of resistance.

Aspia Space A BSTRACT We present Dargana, a fine-tuned variant of the EarthPT time-series foundation model that achieves specialisation using < 3% of its pre-training data volume and 5% of its pre-training compute. Dargana is fine-tuned to generate regularly updated classification of tree canopy cover at 10 m resolution, distinguishing conifer and broadleaved tree types. Using Cornwall, UK, as a test case, the model achieves a pixel-level ROC-AUC of 0.98 and a PR-AUC of 0.83 on unseen satellite imagery. Dargana can identify fine structures like hedgerows and coppice below the training sample limit, and can track temporal changes to canopy cover such as new woodland establishment. Our results demonstrate how pre-trained Large Observation Models like EarthPT can be specialised for granular, dynamic land cover monitoring from space, providing a valuable, scalable tool for natural capital management and conservation.

Man guilty of army veteran hammer attack murder Cumbria PoliceJack Crawley attempted to burn Paul Taylor's body, before burying him in woodland A man who attacked an army veteran he had met for sex and bludgeoned him with a hammer has been found guilty of murder. Paul Taylor, 57, from Annan, Dumfriesshire, went missing last October, with his remains found in a shallow grave in woodland near Carlisle, Cumbria, in May. Jack Crawley, 20, of Carlisle, was found guilty of attacking him and trying to burn his body following a trial at the city's crown court. He will be sentenced on Wednesday. Crawley was also found guilty of the attempted murder of a man in York, who he met on the gay dating app Grindr and also attacked with a hammer, while he was on bail for killing Mr Taylor.

The prototype drone, called FireDrone, could be sent into burning buildings or woodland to assess hazards and provide crucial first-hand data from danger zones. The data would then be sent to first responders to help inform their emergency response. The drone is made of a new thermal aerogel insulation material and houses an inbuilt cooling system to help it withstand temperatures of up to 200 C for ten minutes. Currently at prototype stage, the researchers believe FireDrone could eventually be used to scope out fires for people and extra hazards to bolster firefighting. Principal Investigator Professor Mirko Kovac, Director of the Aerial Robotics Lab at Imperial College London and Head of the Laboratory of Sustainability Robotics at Empa, said: "Until they enter the danger zone, firefighters can't be certain of what or who they'll find, and what challenges they'll encounter. "FireDrone could be sent in ahead to gather crucial information so that responders can prepare accordingly to ...

Drone footage taken by the National Trust has revealed the extent of the devastation being wrought on British woodlands by ash dieback, a deadly fungal infection. The shots taken this autumn show trees dying in Hanging Wood, part of the Trust-administered Hughenden Estate in Buckinghamshire. Some 300 ash trees on the estate will need to be felled this year in the interests of public safety -- with many more left to decay and create homes for wildlife. However, the Trust warned, this is a mere fraction of the 40,000-odd trees that will need cutting down in total across the lands they manage in the UK. Ash dieback -- though to have originated in Asia before spreading as a result of the global plant trade -- is caused by the fungus Hymenoscyphus fraxineus.

Gating is a key technique used for integrating information from multiple sources by long short-term memory (LSTM) models and has recently also been applied to other models such as the highway network. Although gating is powerful, it is rather expensive in terms of both computation and storage as each gating unit uses a separate full weight matrix. This issue can be severe since several gates can be used together in e.g. an LSTM cell. This paper proposes a semi-tied unit (STU) approach to solve this efficiency issue, which uses one shared weight matrix to replace those in all the units in the same layer. The approach is termed "semi-tied" since extra parameters are used to separately scale each of the shared output values. These extra scaling factors are associated with the network activation functions and result in the use of parameterised sigmoid, hyperbolic tangent, and rectified linear unit functions. Speech recognition experiments using British English multi-genre broadcast data showed that using STUs can reduce the calculation and storage cost by a factor of three for highway networks and four for LSTMs, while giving similar word error rates to the original models.

Vanishing long-term gradients are a major issue in training standard recurrent neural networks (RNNs), which can be alleviated by long short-term memory (LSTM) models with memory cells. However, the extra parameters associated with the memory cells mean an LSTM layer has four times as many parameters as an RNN with the same hidden vector size. This paper addresses the vanishing gradient problem using a high order RNN (HORNN) which has additional connections from multiple previous time steps. Speech recognition experiments using British English multi-genre broadcast (MGB3) data showed that the proposed HORNN architectures for rectified linear unit and sigmoid activation functions reduced word error rates (WER) by 4.2% and 6.3% over the corresponding RNNs, and gave similar WERs to a (projected) LSTM while using only 20%--50% of the recurrent layer parameters and computation.

The mixture of multivariate Bernoulli distributions (MMB) is a statistical model for high-dimensional binary data in widespread use. Recently, the MMB has been used to model the sequence of packet receptions and losses of wireless links in sensor networks. Given an MMB trained on long data traces recorded from links of a deployed network, one can then use samples from the MMB to test different routing algorithms for as long as desired. However, learning an accurate model for a new link requires collecting from it long traces over periods of hours, a costly process in practice (e.g. limited battery life). We propose an algorithm that can adapt a preexisting MMB trained with extensive data to a new link from which very limited data is available. Our approach constrains the new MMB's parameters through a nonlinear transformation of the existing MMB's parameters. The transformation has a small number of parameters that are estimated using a generalized EM algorithm with an inner loop of BFGS iterations. We demonstrate the efficacy of the approach using the MNIST dataset of handwritten digits, and wireless link data from a sensor network. We show we can learn accurate models from data traces of about 1 minute, about 10 times shorter than needed if training an MMB from scratch.