Digital innovations for monitoring sustainability in food systems.

Monitoring systems that incentivize, track and verify compliance with social and environmental standards are widespread in food systems. In particular, digital monitoring approaches using remote sensing, machine learning, big data, smartphones, platforms and blockchain are proliferating. The increasing use and availability of these technologies put us at a critical juncture to leverage these innovations for enhanced transparency, fairness and open access, rather than descending into a dystopian landscape of digital surveillance and division perpetuated by a powerful few.

Multispecies deep learning using citizen science data produces more informative plant community models.

In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. Here, we map fine-grained spatiotemporal distributions for thousands of species, using deep neural networks (DNNs) and ubiquitous citizen science data. Based on 6.

A high-resolution canopy height model of the Earth.

The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestrial ecosystems, mitigate climate change and prevent biodiversity loss. Here we present a comprehensive global canopy height map at 10 m ground sampling distance for the year 2020.

Fine-Grained Species Recognition With Privileged Pooling: Better Sample Efficiency Through Supervised Attention.

We propose a scheme for supervised image classification that uses privileged information, in the form of keypoint annotations for the training data, to learn strong models from small and/or biased training sets. Our main motivation is the recognition of animal species for ecological applications such as biodiversity modelling, which is challenging because of long-tailed species distributions due to rare species, and strong dataset biases such as repetitive scene background in camera traps. To counteract these challenges, we propose a visual attention mechanism that is supervised via keypoint annotations that highlight important object parts.

Gating Revisited: Deep Multi-Layer RNNs That can be Trained.

We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM [16] and GRU [10] while being more robust against vanishing or exploding gradients. Stacking recurrent units into deep architectures suffers from two major limitations: (i) many recurrent cells (e.g.