Vision is essential for human navigation. The World Health Organization (WHO) estimates that 43.3 million people were blind in 2020, and this number is projected to reach 61 million by 2050. Modern scene understanding models could empower these people by assisting them with navigation, obstacle avoidance and visual recognition capabilities. The research community needs high quality datasets for both training and evaluation to build these systems. While datasets for autonomous vehicles are abundant, there is a critical gap in datasets tailored for outdoor human navigation. This gap poses a major obstacle to the development of computer vision based Assistive Technologies. To overcome this obstacle, we present SANPO, a large-scale egocentric video dataset designed for dense prediction in outdoor human navigation environments. SANPO contains 701 stereo videos of 30+ seconds captured in diverse real-world outdoor environments across four geographic locations in the USA. Every frame has a high resolution depth map and 112K frames were annotated with temporally consistent dense video panoptic segmentation labels. The dataset also includes 1961 high-quality synthetic videos with pixel accurate depth and panoptic segmentation annotations to balance the noisy real world annotations with the high precision synthetic annotations. SANPO is already publicly available and is being used by mobile applications like Project Guideline to train mobile models that help low-vision users go running outdoors independently. To preserve anonymization during peer review, we will provide a link to our dataset upon acceptance.

We present here the development and initial results of a low latency GNSS-R flood detection and visualisation framework, the Flood Assessment Spatial Triage. This uses airborne GNSS-Reflectometry data which are routinely collected as part of the Rongowai mission, hosted on an Air New Zealand Q300 domestic aircraft. The framework is able to detect flooding with low latency after the aircraft lands, through rapid processing of the Level-0 data which combines reflected GNSS signals with terrain data. These may be useful for flood preparedness and response, and may additionally help to guide the tasking of imaging assets.

We developed a random forest model to predict soil moisture at the field scale (∼1 km), combining 6.5 years of CYGNSS Global Navigation Satellite System Reflectometry (GNSS-R) data with observations from 1049 gauges which are part of the International Soil Moisture Network. Several relevant predictor variables were developed, including geophysical parameters related to soil properties, topography, land cover and climatology, and dynamic variables including precipitation from the Global Precipitation Mission and evapotranspiration derived from MODIS temperature. The model achieved good accuracy for predicted soil moisture (R2= 0.68; RMSE = 0.064 mm/mm), which was further improved through postprocessing using a linear regression model of the residuals for bias correction (R2= 0.82; RMSE = 0.048 mm/mm). Our work demonstrates the potential for field-scale prediction of soil moisture from GNSS-R data, which may be further improved through enhanced co-variates, and is applicable to recent and near-future GNSS-R missions such as Rongowai and HydroGNSS.

We identify morphisms of strong profunctors as a categorification of quantum supermaps. These black-box generalisations of diagrams-with-holes are hence placed within the broader field of profunctor optics, as morphisms in the category of copresheaves on concrete networks. This enables the first construction of abstract logical connectives such as tensor products and negations for supermaps in a totally theory-independent setting. These logical connectives are found to be all that is needed to abstractly model the key structural features of the quantum theory of supermaps: black-box indefinite causal order, black-box definite causal order, and the factorisation of definitely causally ordered supermaps into concrete circuit diagrams. We demonstrate that at the heart of these factorisation theorems lies the Yoneda lemma and the notion of representability.

Earth observations are pivotal for developing informed, evidence-based sustainable practices and are of direct consequence to four of the seventeen UN Sustainable Development Goals (12–15). Among these, Earth observations using signals of opportunity such as GNSS-R have significantly evolved in recent years including expanding efforts to consider geophysical retrievals over complex and heterogeneous surfaces, primarily from space-borne missions. Airborne GNSS-R however has the advantages of higher resolution and signal strength and as such can provide critical data to inform algorithm development for complex environments. This paper presents the upcoming airborne Rongowai mission whereby Air New Zealand will fly a NASA-developed next-generation GNSS-R receiver as a unique international collaboration. Rongowai promises to deliver rich environmental records for sustainable land-use and water management including coastal and open ocean over many years at unprecedented spatial and temporal resolutions. We present representative coverage, and methodology for product development with specific focus on soil-moisture as it relates to land-use and water management gather

Over two decades after the scholarly interventions that coalesced into ‘critical GIS’ as a field within GIScience, critical GIS remains underdeveloped in conversations on teaching and learning. The literature on GIScience education has emphasized content more than pedagogies – what to teach versus how to teach to move students toward particular learning objectives. This emphasis is reflected in dominant curricular approaches to critical GIS, in which questions around the complicated origins and complicit social, political, and economic relationships of GIS are taken up as discrete topics, tacked onto instruction that otherwise prioritizes technical dimensions of GIScience. We argue that GIScience coursework must resist such modularization by approaching critical GIS not as a set of topics, but an orientation to GIS praxis that ‘does’ GIS from within a questioning stance, to ask how we know. We outline specific curricular shifts and teaching practices we have used to foster this orientation in GIS students, offering ways of continually engaging students in practicing this orientation as they learn strong technical GIScience content. Finally, we trace the successes, challenges, and tensions sparked by these critical GIS pedagogies, drawing on student evaluation comments from our courses and reflecting on broader implications for GIScience instructors and geography faculty.

This paper develops a new analytic model for the performance and reliability analysis of safety-related message broadcast in vehicular ad hoc networks (VANETs) at rural intersections. First, a semi-Markov process (SMP) model for characterizing channel behavior of IEEE 802.11 based VANET is deployed to interact with the M/G/1 queue through fixed-point iteration. Then, the mean transmission delay between two nodes is derived. Furthermore, given two communicating nodes placed at the same intersection, the node reception probability that a node successfully receives the broadcast message from a sending node is computed. Consequently, the packet reception ratios are derived through integration of the node reception probabilities over the intended range of the sending node. The analytical model takes intersection geometry, IEEE 802.11 backoff counter process, hidden terminal problem, and non-saturated message arrival into account. From the obtained numerical results under various network parameters, the new model is validated and new observations are obtained.