I think the term is adequate: think of things like google flu trends where the searches of people are used for detecting incidence of flu. Or the Tweet Earthquake Dispatch http://earthquake.usgs.gov/earthquakes/ted/ where the tweets of people can be used to detect actual earthquakes.

You are true that, in most cases, it's not people the sensor but the tools they are wearing, but I feel the term fits well the field of application (sensors around us, with us, or us as sensors).

Yea, there are certainly many ways to look into this broadly defined notion (1) people wearing sensors, (2) sensors detecting people, (3) people sensing their surroundings and reporting about the findings. MORE?

(1) is quite an Internet of Things (IoT) playing field where people become platforms for a hole lot of diverse sensors, yesterday I read about latest fashion products in which sensors are included in textiles and are reporting a lot about personal conditions and movements. (2) Is more of an surveillance (big brother) type of thing, where for example more and more public cameras apply face detection etc. (3) at the first glance is closest to the idea of 'people as sensors' or VGI (as Irene spotted). At the second look it pushes the metaphor pretty much at its limits, because people do not just transfer analogue signals (inputs) to digital outputs, they apply quite some processing and interpretation in between; all depending on their knowledge, current thinking, etc. Even then, we might distinguish between user contributed content, which is provided voluntarily to a specific community and those not, as well as implicit or explicit 'sensing'. With the latter I want to say that you might report on something specific, e.g. occurrences of birds on birdwatch.co.uk, or they report on anything and others try to extract something reasonable, e.g. earthquakes (as mentioned by Fernando).

All raise different issues, or at least put different weights one them. All raise issues of privacy, security and informing people about what they are exposing and to whom.

Could you think of other issues (or benefits), e.g. related to ethics, use of social media, Web 2.0/3.0, eGovernment...

Check out the term "citizen sensing" for human-in-the-loop sensing, and how that contrasts with "physical sensing" or "machine sensing".

Few interesting pointers to follow up:

https://plus.google.com/100317718273506004830/posts/YGeUjXgyMRM

https://plus.google.com/107520958318106253576/posts/UVkYQBzabeb

https://plus.google.com/105103058358743760661/posts/c4cgkCt92MV

http://dl.acm.org/citation.cfm?id=1592076

Indeed, this view on "people as sensors" is quite widely shared. Following Amit's "citizen sensing" basically includes this aspect plus the Internet of Things (IoT) dimension (correct me if I am wrong).

The point is, people can provide information in many diverse ways (targeted to a domain, such as bird watching, as part of a general social media platform, etc.). Still, each time a human is involved, you do not receive monitoring data, but some information which was processed by the human brain (under many influences). If people carry sensors in any form - and this is the IoT aspect - you can collect raw data. From all of these sources - and their combination - you might be able to extract interesting information, including state of the environment, but also human behavior.

Is this fair enough?

Sven: The distinction that machine sensed data represents raw data is true to an extent-- we now increasingly do semantic (machine) perception which automatically apply background knowledge (created by humans such as through domain specific ontologies, or automatically mined from collective work such as Wikipedia), combine with other relevant data, and convert observations (raw data) into perceptions [1][2], or semantic abstractions closer to concepts humans may be interested in. On the citizen sensing side, human intermediation brings a very extensive amount of richness and complexity-- just a few of them can be found in figure 1 at [3].

[1] http://knoesis.org/library/resource.php?id=1682

[2] http://knoesis.org/library/resource.php?id=1682

[3] http://knoesis.org/library/resource.php?id=1869

Exactly, this is the next logical step. Adding a 'perceptual' layer for machines might (i) increase our understanding how humans generate information; and (ii) provide the necessary processing of an increasing amount of raw inputs to become better digestible and useful for people. However, including semantic and data mining activities still leaves the impression of some magic happening... it might be important to record 'metadata' on how the machine inferred the new facts.

Thanks for pointing to the figure. It certainly illustrates the richness and complexity, but the arrangement confuses me. How do the axes relate to the circles and what is the logic in arranging the red and black text?

By the way, the links [1] and [2] above are identical. Was this intended?

Sorry, the second link should have been

[2] http://knoesis.org/library/resource.php?id=1761

Re: your question on colors:

essentially if you go though the evolution of how we went to different levels

of depth (correspondingly metadata) fo social data/citizen sensing,

you will see three "generations"

Gen 1: Purple: spatial, temporal, thematic

Gen 2: Red: People, content (overlap with "thematic"), network

Gen 3: Black (also call "subjectivity" dimention): sentiment, emotion, intention; social, cultural, behavioral

Easier access:

http://wiki.knoesis.org/index.php/Twitris

Love to see guys on this list at ISTAS13 -- check out http://veillance.me and the community on G+ https://plus.google.com/communities/103231540251769845150 We are talking people as sensors and the way they interconnect with their lifeworld.

Katina-- thanks for the links, wasn't aware of ISTAS13- some pretty well known names. I will have an opportunity to give my own take in these directions at: http://aida.ii.uam.es/wims13/keynotes.php (PhysicalCyberSocial computing) and my earlier/related thoughts is under Computing for Human Experience at http://knoesis.org/node/266 (also have a video of the talks I have given on the topic). Cheers.

ps: hope I did not annoy anyone with self-pointers.

The concept of 'people as sensors' in geospatial sciences reflects the burgeoning interest in leveraging human-generated data for enhanced environmental sensing and data collection. This approach utilizes the ubiquity of mobile devices, wearable technology, and social media as a means to gather geospatial data, including imagery, sensor data from devices, and textual information from social networks. The benefits of considering people as sensors include enriched datasets for environmental monitoring, real-time data collection, and the democratization of data gathering. However, it also raises questions about privacy, data accuracy, and the ethics of using human-generated data for scientific purposes.

The work in [1] discusses a related but technically different concept, focusing on the integration of radar sensing and communication technologies. This research is part of the broader field of Integrated Sensing and Communication (ISAC), which aims to unify the functionality of sensing (radar, lidar, etc.) and communications into a single platform. The principle behind ISAC and the concept of 'people as sensors' shares a foundational similarity in their pursuit of multipurpose use of technologies and the efficient utilization of resources (spectrum, hardware, or human-related data). Specifically, [1] addresses the technical challenges and solutions in merging radar and communication functions effectively, focusing on enhancing radar sensing capabilities through communication infrastructures and vice versa. While 'people as sensors' primarily leverage human activities and mobile devices for data collection, ISAC, as discussed in [1]., emphasizes the technical integration within hardware systems to achieve dual-functionality, highlighting the importance of designing systems that can perform both sensing and communication tasks without compromising the performance of either function.

Both concepts, although applied in different contexts, highlight a trend towards exploiting available resources—whether human-related or technical for multiple purposes, thereby maximizing efficiency and functionality. While 'people as sensors' addresses the power of human mobility and social connectivity for geospatial data collection, ISAC focuses on the convergence of communication and sensing technologies for applications like autonomous driving, environmental monitoring, and smart cities.

Reference:

[1] Akhileswar Chowdary Gaddam, Ahmad Bazzi and Marwa Chafii, “On Hybrid Radar Fusion for Integrated Sensing and Communication,” in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2024.3357573