Brostow

Community-based conservation (CBC) initiatives aim to reconcile biodiversity protection with local livelihoods, yet their effectiveness in protecting wildlife remains uncertain, often hinging on local management ^1,2 . We evaluated a globally significant CBC model in Kenya’s Greater Maasai Mara Ecosystem (GME), where conservancies, run jointly by Maasai landowners and the tourism sector, employ rotational cattle grazing to support both wildlife and pastoralism ^3,4 . Using a ∼1200 km ² grid of 180 camera traps across gradients of livestock pressure in Maasai Mara National Reserve and three conservancies in 2018, we collected and analysed over 2 million images with a customised AI-powered pipeline. We found a positive impact of observed cattle pressure on mammal community occupancy and species richness, except for at the highest levels of cattle grazing. However, sheep and goat grazing and proximity to infrastructure had a negative impact. These results provide evidence that wildlife and pastoralism can coexist under community-led stewardship ⁵ , but only with active management and targeted control of emerging threats. AI tools such as our image classifier may contribute to more adaptive community-led management of these areas ⁶ . As conservation policy shifts beyond formal protected areas, our findings support CBC as a scalable model for conserving biodiversity within working landscapes, offering a pathway to meet global targets while maintaining local livelihoods ⁷ .

It can be easy and even fun to sketch humans in different poses. In contrast, creating those same poses on a 3D graphics "mannequin" is comparatively tedious. Yet 3D body poses are necessary for various downstream applications. We seek to preserve the convenience of 2D sketching while giving users of different skill levels the flexibility to accurately and more quickly pose\slash refine a 3D mannequin. At the core of the interactive system, we propose a machine-learning model for inferring the 3D pose of a CG mannequin from sketches of humans drawn in a cylinder-person style. Training such a model is challenging because of artist variability, a lack of sketch training data with corresponding ground truth 3D poses, and the high dimensionality of human pose-space. Our unique approach to synthesizing vector graphics training data underpins our integrated ML-and-kinematics system. We validate the system by tightly coupling it with a user interface, and by performing a user study, in addition to quantitative comparisons.

Cities support unique and valuable ecological communities, but understanding urban wildlife is limited due to the difficulties of assessing biodiversity. Ecoacoustic surveying is a useful way of assessing habitats, where biotic sound measured from audio recordings is used as a proxy for population abundance and/or activity. However, existing algorithms systematically over and underestimate measures of biotic activity in the presence of typical urban non‐biotic sounds in recordings. We develop CityNet, a deep learning system using convolutional neural networks (CNNs), to measure audible biotic (CityBioNet) and anthropogenic (CityAnthroNet) acoustic activity in cities. The CNNs were trained on a large dataset of annotated audio recordings collected across Greater London, UK. Using a held‐out test dataset, we compare the precision and recall of CityBioNet and CityAnthroNet separately to the best available alternative algorithms: four Acoustic Indices: Acoustic Complexity Index, Acoustic Diversity Index, Bioacoustic Index, and Normalised Difference Soundscape Index, and a state‐of‐the‐art bird call detection CNN (bulbul). We also compare the effect of non‐biotic sounds on the predictions of CityBioNet and bulbul. Finally we apply CityNet to describe acoustic patterns of the urban soundscape in two sites along an urbanisation gradient. CityBioNet was the best performing algorithm for measuring biotic activity in terms of precision and recall, followed by bulbul, whereas the Acoustic Indices performed worst. CityAnthroNet outperformed the Normalised Difference Soundscape Index, but by a smaller margin than CityBioNet achieved against the competing algorithms. The CityBioNet predictions were impacted by mechanical sounds, whereas air traffic and wind sounds influenced the bulbul predictions. Across an urbanisation gradient, we show that CityNet produced realistic daily patterns of biotic and anthropogenic acoustic activity from real‐world urban audio data. Using CityNet, it is possible to automatically measure biotic and anthropogenic acoustic activity in cities from audio recordings. If embedded within an autonomous sensing system, CityNet could produce environmental data for cites at large‐scales and facilitate investigation of the impacts of anthropogenic activities on wildlife. The algorithms, code and pretrained models are made freely available in combination with two expert‐annotated urban audio datasets to facilitate automated environmental surveillance in cities.

3D Convolutional Neural Networks are sensitive to transformations applied to their input. This is a problem because a voxelized version of a 3D object, and its rotated clone, will look unrelated to each other after passing through to the last layer of a network. Instead, an idealized model would preserve a meaningful representation of the voxelized object, while explaining the pose-difference between the two inputs. An equivariant representation vector has two components: the invariant identity part, and a discernable encoding of the transformation. Models that can't explain pose-differences risk "diluting" the representation, in pursuit of optimizing a classification or regression loss function. We introduce a Group Convolutional Neural Network with linear equivariance to translations and right angle rotations in three dimensions. We call this network CubeNet, reflecting its cube-like symmetry. By construction, this network helps preserve a 3D shape's global and local signature, as it is transformed through successive layers. We apply this network to a variety of 3D inference problems, achieving state-of-the-art on the ModelNet10 classification challenge, and comparable performance on the ISBI 2012 Connectome Segmentation Benchmark. To the best of our knowledge, this is the first 3D rotation equivariant CNN for voxel representations.