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What role will ubiquitous sensing play in our understanding and experience of ecology in the future? What opportunities are created by weaving a continuously sampling, geographically dense web of sensors into the natural environment, from the ground up? In this article, we explore these questions holistically, and present our work on an environmental sensor network designed to support a diverse array of applications, interpretations, and artistic expressions, from primary ecological research to musical composition. Over the past four years, we have been incorporating our ubiquitous sensing framework into the design and implementation of a large-scale wetland restoration, creating a broad canvas for creative exploration at the landscape scale. The projects we present here span the development and wide deployment of custom sensor node hardware, novel web services for providing real-time sensor data to end user applications, public-facing user interfaces for open-ended exploration of the data, as well as more radical UI modalities, through unmanned aerial vehicles, virtual and augmented reality, and wearable devices for sensory augmentation. From this work, we distill the Networked Sensory Landscape, a vision for the intersection of ubiquitous computing and environmental restoration. Sensor network technologies and novel approaches to interaction promise to reshape presence, opening up sensorial connections to ecological processes across spatial and temporal scales.

Personal assistants are becoming more pervasive in our envi-ronments but still do not provide natural interactions. Their lack of realism in term of expressiveness and their lack of visual feedback can create frustrating experiences and make users lose patience. In this sense, we propose an end-to-end trainable neural architecture for text-driven 3D mouth animations. Previous works showed such architectures provide better realism and could open the door for integrated affective Human Computer Interface (HCI). Our study shows that such visual feedback improves users’ comfort for 78%of the candidates significantly while slightly improving their time perception.

We describe 'Tidzam', an application of deep learning that leverages a dense, multimodal sensor network installed at a large wetland restoration performed at Tidmarsh, a 600-acre former industrial-scale cranberry farm in Southern Massachusetts. Wildlife acoustic monitoring is a crucial metric during post-restoration evaluation of the processes, as well as a challenge in such a noisy outdoor environment. This article presents the entire Tidzam system, which has been designed in order to identify in real-time the ambient sounds of weather conditions as well as sonic events such as insects, small animals and local bird species from microphones deployed on the site. This experiment provides insight on the usage of deep learning technology in a real deployment. The originality of this work concerns the system's ability to construct its own database from local audio sampling under the supervision of human visitors and bird experts.

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Ubiquitous touch sensing surfaces are largely influenced by touchscreens' look and feel and fail to express the physical richness of existing surrounding materials. We introduce Sensurfaces, a plug-and-play electronic module that allows to rapidly experiment with touch-sensitive surfaces while preserving the original appearance of materials. Sensurfaces is composed of plug-and-play modules that can be connected together to expand the size and number of materials composing a sensitive surface. The combination of Sensurfaces modules allows the creation of small or large multi-material sensitive surfaces that can detect multi-touch but also body proximity, pose, pass, or even human steps. In this paper, we present the design and implementation of Sensurfaces. We propose a design space describing the factors of Sensurfaces interfaces. Then, through a series of technical evaluations, we demonstrate the capabilities of our system. Finally, we report on two workshops validating the usability of our system.

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