Road pavement detection and segmentation are critical for developing autonomous road repair systems. However, developing an instance segmentation method that simultaneously performs multi-class defect detection and segmentation is challenging due to the textural simplicity of road pavement image, the diversity of defect geometries, and the morphological ambiguity between classes. We propose a novel end-to-end method for multi-class road defect detection and segmentation. The proposed method comprises multiple spatial and channel-wise attention blocks available to learn global representations across spatial and channel-wise dimensions. Through these attention blocks, more globally generalised representations of morphological information (spatial characteristics) of road defects and colour and depth information of images can be learned. To demonstrate the effectiveness of our framework, we conducted various ablation studies and comparisons with prior methods on a newly collected dataset annotated with nine road defect classes. The experiments show that our proposed method outperforms existing state-of-the-art methods for multi-class road defect detection and segmentation methods.

Ensuring traffic safety is crucial, which necessitates the detection and prevention of road surface defects. As a result, there has been a growing interest in the literature on the subject, leading to the development of various road surface defect detection methods. The methods for detecting road defects can be categorised in various ways depending on the input data types or training methodologies. The predominant approach involves image-based methods, which analyse pixel intensities and surface textures to identify defects. Despite popularity, image-based methods share the distinct limitation of vulnerability to weather and lighting changes. To address this issue, researchers have explored the use of additional sensors, such as laser scanners or LiDARs, providing explicit depth information to enable the detection of defects in terms of scale and volume. However, the exploration of data beyond images has not been sufficiently investigated. In this survey paper, we provide a comprehensive review of road surface defect detection studies, categorising them based on input data types and methodologies used. Additionally, we review recently proposed non-image-based methods and discuss several challenges and open problems associated with these techniques.

India has been witnessing a steady increase in smartphone penetration since 2016 after Reliance Jio introduced inexpensive internet plans. Much of HCI research in the Global South has been conducted before smartphones became more widespread. More recent work on smartphone use in India has been either domain-focused or studied specific features. In this work, we investigate how emergent users from low-income communities in India currently use their smartphones, and what they use them for. We draw on semi-structured interviews with emergent smartphone users across rural and urban India demonstrating their experiences and challenges related to low- textual and digital literacy, infrastructure, privacy, and motivations of use. Our findings revealed that while there is a lack of understanding of basic features such as accounts and passwords, there is sophisticated use spanning user-generated media, remote education, skilling, etc. We close with recommendations for future research and design for emergent smartphone users.

As community-driven organizations sought to support their constituents through the COVID-19 crisis, many drew on digital volunteers to expand their capacity and reach. However, coordinating the efforts of virtual volunteers is a challenging task with few empirical studies of the associated risks and best practices. In this paper, we report on the activities of CGNet Swara, a citizen journalism platform that published 401 distress calls from vulnerable communities stranded in India due to the imposition of a nationwide lockdown. CGNet mobilized 11 digital volunteers to help these contributors over a period of nearly 2 months. We found that a lack of proper guidance to digital volunteers and outdated organizational policies resulted in demonstrable harms to vulnerable communities. We discuss risks that are inherent in collaborations between organizations extending themselves to crisis response and emergent groups of digital volunteers, and how they can be mitigated by real-time monitoring and development of standard operating procedures relating to impact metrics, verification standards and disclosure policies.

Increasing penetration of Internet-enabled smartphones in low-resource areas makes them an attractive platform for engaging emerging users. In this paper, we demonstrate how a voice forum for citizen journalism in rural India– previously accessible via an Interactive Voice Response (IVR) system– can be naturally supported and enriched using a chatbot. Implemented using the WhatsApp Business API, the bot enables submission of both audio (with or without image) and video stories. Following review by moderators, stories are published on a website and social media sites, and can also be browsed interactively using the WhatsApp bot. This multi-way, intermediated model of communication expands the scope and functionality of typical WhatsApp groups while offering significant cost savings relative to IVR systems. In the first 9 weeks of a long-term deployment, the bot demonstrated high usability and acceptance and resulted in 218 published stories from 27 users.

In the Global South, mobile airtime payment has emerged as a popular way to incentivize different research studies, including ones on survey completion or disseminating information to people. Building on this literature, we report deployment experiences from three different studies in India that used airtime incentives. The first was used to promote awareness about HIV/AIDS, the second for promoting awareness and surveying preparedness for an upcoming election, and the third to measure learning and encourage people to vote in a conflict-hit region for a different election. Unlike past work, we found that a delivery mechanism that focuses on asking questions first, rather than presenting a tutorial and then asking questions, worked well in practice. In addition, we found multiple challenges in adoption of the technology and tried different ways to incentivize peer sharing of our system. Between the three deployments, we also addressed other technical and human-centered challenges such as delayed airtime payments and people using the system on behalf of someone else. We hope that our experiences and insights can be helpful to others seeking to deploy applications that utilize mobile airtime payments for learning, sharing, and survey response.

The primary obstacle to developing technologies for low-resource languages is the lack of usable data. In this paper, we report the adaption and deployment of 4 technology-driven methods of data collection for Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India. In the process of data collection, we also help in its revival by expanding access to information in Gondi through the creation of linguistic resources that can be used by the community, such as a dictionary, children’s stories, an app with Gondi content from multiple sources and an Interactive Voice Response (IVR) based mass awareness platform. At the end of these interventions, we collected a little less than 12,000 translated words and/or sentences and identified more than 650 community members whose help can be solicited for future translation efforts. The larger goal of the project is collecting enough data in Gondi to build and deploy viable language technologies like machine translation and speech to text systems that can help take the language onto the internet.

In rural parts of the developing world, spreading awareness about critical issues in health, governance, and other topics is challenging and costly. Traditional media such as print, radio and TV each have limitations and offer little guarantee that new information is absorbed or retained by the target population. This paper describes Learn2Earn, a system that leverages mobile payments to bolster public awareness campaigns in rural India. Users call an Interactive Voice Response (IVR) system, listen to a brief audio tutorial, and take a multiple-choice quiz to check their understanding. People who pass the quiz receive a mobile top-up (about $0.14) and have the opportunity to earn additional credits by referring others to the system. We describe a pilot deployment of Learn2Earn in rural India that spread via word-of-mouth to over 15,000 people within seven weeks. Usage was concentrated among young men, many of them students. In a mixed-methods study, we draw upon call logs, electronic surveys, qualitative interviews, and other sources of data to suggest that Learn2Earn could be an effective way to build awareness about important topics.