Summary:

The main objective of this paper is to investigate the feasibility of using crowd workers to locate and assess sidewalk accessibility problems using Google Street View imagery. To achieve this goal, the author conducted two studies to examine the feasibility of finding, labeling sidewalk accessibility problems. The paper uses the results of the first study to prove the possibility of labeling tasks, define what does good labeling performance like, and also provide verified ground truth labels that can be used to assess the performance of crowd workers. Then, the paper evaluates the annotation correctness from two discrete levels of granularity: image level and pixel level. The previous evaluation check for the absence or presence of a label and the later examination in a more precise way, which related to image segmentation work in computer vision. Finally, the paper talked about the quality control mechanisms, which include statistical filtering, an approach for revealing effective performance thresholds for eliminating poor quality turkers, and verification interface, which is a subjective approach to validates labels.

Reflection:

The most impressive point in this paper is the feasibility study, study 1. Since this study not only investigates the feasibility of the labeling work but also provides a standards of good labeling performance and indicate the validated ground truth labels, which can be used to evaluate the crowd worker’s performance. This pre-study provides all the clues, directions, and even the evaluation matrix for the later experiment. It provides the most valuable information for the early stage of the research with a very low workload and effort. I think sometimes it is a research issue that we put a lot of effort into driving the project forward instead of preparing and investigate the feasibility. As a result, we stuck by some problems that we can foresee if we conduct a pre-study.

However, I don’t think the pixel-level assessment is a good idea for this project. Because the labeling task does not require such a high accuracy for the inaccessible area, and it is to accurate to mark the inaccessible area with the unite of the pixel. As the table indicated in the paper’s results of pixel-level agreement analysis, the area overlaps for both binary classification, and multiclass classification are even no more than 50%. Also, though, the author thinks even a 10-15% overlap agreement at the pixel level would be sufficient to localize problems in images, this makes me more confused about whether the author wants to make an accurate evaluation or not.

Finally, considering our final project, it is worth to think about the number of crowd workers that we need for the task. We need to think about the accuracy of turkers per job. The paper made a point that performance improves with turker count, but these gains diminish in magnitude as group size grows. Thus, we might want to figure out the trade-off between accuracy and cost so that we can have a better idea of choice for hiring the workers.

Questions:

• What do you think about the approach for this paper? Do you believe a pre-study is valuable? Will you apply this in your research?
• What do you think about the matrix the author used for evaluating the labeling performance? What else matrix would you like to apply in assessing the rate of overlap area?
• Have you ever considered how many turkers you need to hire would meet your accuracy need for the task? How do you evaluate this number?

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Summary

Hara, Le, and Froehlich developed an interface that uses Google Street View to identify accessibility issues in city sidewalks. They then perform a study using three researchers and 3 accessibility experts (wheelchair users) to evaluate their interface. This severed as both a way to assess usability issues with their interface as well as a ground truth to verify the results of their second study. That study involved launching crowdworking tasks to identify accessibility problems as well as categorizing what type each problem is. Over 7,517 Mechanical Turk HITs they found that crowdworkers could identify accessibility problems 80.6% of the time and could correctly classify the problem type 78.3% of the time. Combining their approach with a majority voting scheme, they raised these values to 86.9% and 83.8%.

Personal Reflection

Their first step to see if their solution was even feasible seemed like an odd study. Their users were research members and experts, both of which are theoretically more driven than a typical crowdworker. Furthermore, I felt like internal testing and piloting would be more appropriate than a soft-launch like this. While they do bring up that they needed a ground truth to contextualize their second study, I initially felt that this should then be performed by only experts and not as a complete preliminary study. However, as I read more of the paper, I felt that the comparison between the groups (experts vs. researchers) was relevant as it highlighted how wheelchair bound people and able-bodied people can see situations differently. They could not have collected this data on Mechanical Turk alone as they couldn’t guarantee that they were recruiting wheelchair bound participants otherwise.

It was also good to see the human-AI collaboration highlighted in this study. That they’re using the selection (and subsequent images generated by those selections) as training data for a machine learning algorithm, it should lessen the need for future work.

Their pay level also seemed very low at 1-5 cents per image. Even assuming a selection and classification takes only 10 seconds, their total page loading only takes 5 seconds, and they always get 5 cents per image, that’s $12 an hour for ideal circumstances.

The good part of this research is that it cheaply identifies problems quickly. This can be used to identify a large amount of issues and save time in deploying people to fix issues that are co-located in the same area rather than deploying people to find issues and then solve them with lesser potential coverage. It also solves a public need for a highly vulnerable population which makes their solution’s impact even better.

Lastly, it was good to see how the various levels of redundancy impacted their results. The falloff from increasing past 5 workers was harsher than I expected, and the increase in identification is likely not worth doubling the cost of these tasks.

Questions

1. What other public needs could a Google Street View/crowdsourcing hybrid solve?
2. What are the tradeoffs for the various stakeholders involved in solutions like this? (The people who need the fixes, the workers who typically had to identify these problems, the workers who are deployed to maintain the identified areas, and any others)
3. Should every study measure the impact of redundancy? How might redundant workers affect your own projects?

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Summary

The paper by Hara et al. attempts to address the problem of sidewalk accessibility by using crowd workers to label the data. The authors had different contributions in addition to just making crowd workers label images. They conduct two studies, a feasibility study and an online crowdsourcing study using AMT. The first study aims to find out how practical it is to label sidewalks using reliable crowd workers (experts). This study also gives an idea of the baseline performance and acts as a validated ground truth data. The second study aims to find out the feasibility of using Amazon Mechanical Turks for this task. They have evaluated the accuracy of image-level as well as pixel-level. The authors have conducted a thorough background study on the current sidewalk accessibility issues, the current audit methods, and that of crowdsourcing and image labeling. They were successful in showing that untrained crowd workers could identify and label sidewalk accessibility issues correctly in the google street view imagery. 

Reflection

Combining crowdsourcing and google street view to identify street- level accessibility is essential and useful for people. The paper was an interesting read and the authors described the system well. In the video[1], the authors show the instructions for the workers. The video gave a fascinating insight into how the task was designed for the workers, explaining every labeling task in detail. 

The paper mentions accessibility, but they have restricted their research for wheelchair users. This works for the first study as they are able to label the obstacles correctly, and this gives us the ground truth data for the next study as well as establishes the feasibility of using crowd workers to identify and label accessibility effectively. However, accessibility problems on sidewalks are also faced by other groups like people with reduced vision, etc. I am curious to see how the experiments would differ if the user-group and the need changes?

The experiments are based on google street view, which is not known to be the best at certain times. There are certain apps that help people get real-time updates on traffic while driving like the app Waze [2]. I was wondering if google maps or any other app insert dynamic updates for street walks, it would be beneficial. It would not only help people but also help the authority in determining which sidewalks are frequently used and the most common issues people face. The paper is a bit old. But, newer technology would surely help users. The paper [3] by the same author is a massive advancement in collecting sidewalk accessibility data. This paper is a good read based on the latest technology.

The paper mentions that active feedback to crowd workers would help improve labeling tasks. I think that dynamic, real-time feedback would be immensely helpful. However, I do understand that it is challenging to implement when using crowd workers, but an internal study could be conducted. For this, a pair or more people need to work simultaneously, where one label and the rest give feedback or some other combinations. 

Questions

1. Sidewalk accessibility has been discussed for people with accessibility problems. They have considered people in wheelchairs for their studies. I do understand that such people would be needed for study 1, where labeling is a factor. However, how does the idea extend to people with other accessibility issues like reduced vision?
2. This paper was published in 2013. The authors do mention in the conclusion section that with improvement in GSV and computer vision will overall help. Has any further study been conducted? How much modification of the current system is needed to accommodate the advancement in GSV and computer vision in general? 
3. Can dynamic feedback to workers be implemented? 

References 

[1] https://www.youtube.com/watch?v=aD1bx_SikGo

[2] https://www.waze.com/waze

[3] http://kotarohara.com/assets/Papers/Saha_ProjectSidewalkAWebBasedCrowdsourcingToolForCollectingSidewalkAccessibilityDataAtScale_CHI2019.pdf

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Summation

Taxes in the US are a very divisive topic, and unfortunately, system infrastructures such as road maintenance are left to take the impact. Due to the lack of resources generally allocated, states typically only allocated the necessary amount of resources to fix problems, while generally leaving out accessibility options. This team from the University of Maryland has taken it upon themselves to prototype identification of these lack of accessibility options via crowd sourcing. They developed a system that utilized information from Google Street View and users could identify problems with the road. The next users could also confirm or deny the previous conclusions from previous users. Throughout this, they ran this experiment using 229 images manually collected through Google Street View and once with 3 handicap users, then with 185 various mechanical turkers. Throughout this, they were able to achieve an accuracy of at least 78% compared to the ground truth. Further trimming down the lower ranking turkers raised the accuracy by about 6% at the cost of filtering out 52% of the turkers.

Response

I can appreciate this approach since I believe (as was also stated in the paper) that a manual effort to identify the accessibility problems would cost a lot of money and time. Both of those requirements are typically sticking and stringent points from government contracts. Though they may not be ready to open this kind of availability to crowd workers, the accuracy is creating a stronger argument. Further ensuring better workers, the study also proved that by dropping the number of raw workers available for better results was ultimately fruitful, and potentially may be in alignment with the type of budget the government could provide.

Questions

• Manually creating ground truth from experts would likely be unsustainable, since the cost in that specific requirement would increase. Since I don’t believe you can require a kind of accessibility handicap in Amazon Mechanical Turk, if this kind of work was solely based on Mechanical (or other crowdsourcing tools), would the ground truth ultimately suffer due to the potential lack of experience and expertise?
• This may be an external perspective; however, it seems there is a definitive split of ideas within the paper, creating a system for crowd workers to identify and then creating a system to optimize the potential crowd workers working on the project. Do you think both ideas were equally weighed and spread throughout the paper or the Google Street View system was a means of utilizing the techniques for optimizing the crowd workers?
• Since these images (solely utilizing Google Street View) are likely only taken periodically (due to resource constraints of the Google Street View Cars), the images are highly likely to be older and under change from any recent construction. When there is a delay from the Google Street View pictures, structures and buildings may have changed without getting updated in the system. Do you think there might be enough changes in the streets that the turkers work would become obsolete?

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Authors: Kotaro Hara, Vicki Le, and Jon Froehlich

Summary

This paper discusses the feasibility of using AMT crowd workers to label sidewalk accessibility problems in Google Street View. The authors create ground truth datasets with the help of wheelchair users, and found that Turkers reached an accuracy of 81%. The paper also discusses some quality control and improvement methods, which was shown to be effective i.e. improved the accuracy to 93%. 

Reflection

This paper reminded me of Jeff Bigham’s quote – “Discovery of important problems, mapping them onto computationally tractable solutions, collecting meaningful datasets, and designing interactions that make sense to people is where HCI and its inherent methodologies shine.” It’s a great example for two important things mentioned in the quote : a) discovery of important problems, and b) collecting meaningful datasets. The paper’s contribution mentions that the datasets collected will be used for building computer vision algorithms, and this paper’s workflow involves the potential end-users (wheelchair users) early on in the process. Further, the paper attempts to use Turkers to generate datasets that are comparable in quality to that of the wheelchair users, essentially setting a high quality standard for generating potential AI datasets. This is a desirable approach for training datasets, which can potentially help prevent problems in popular datasets as outlined here: https://www.excavating.ai/. 

The paper also proposed two generalizable methods for improving data quality from Turkers. Filtering out low-quality workers during data collection by seeding in gold standard data may require designing modular workflows, but the time investment may well be worth it. 

It’s great to see how this work evolved to now form the basis for Project Sidewalk, a live project where volunteers can map accessibility areas in the neighborhood.

Questions

1. What’s your usual process for gathering datasets? How is it different from this paper’s approach? Would you be willing to involve potential end-users in the process? 
2. What would you do to ensure quality control in your AMT tasks? 
3. Do you think collecting more fine-grained data for training CV algorithms will come at a trade-off for the interface not being simple enough for Turkers?

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Summary

The authors try to assess the accessibility of sidewalks by hiring AMT workers to analyze Google Street View images. Traditionally, sidewalk assessment is conducted in person via street audits which are  highly labor intensive and expensive or by reporting calls from citizens. The authors propose using their system as an alternative for a proactive solution to this issue. They perform two studies:

• A feasibility study (Study 1): examines the feasibility of the labeling task with six dedicated labelers including three wheelchair users
• A crowdsourcing study (Study 2): investigates the comparative performance of turkers

In study 1, since labeling sidewalk accessibility problems is subjective and potentially ambiguous, the authors investigate the viability of labeling across two groups:

• Three members of the research team
• Three wheelchair users – accessibility experts

They use the results of study 1 to provide ground truth labels to evaluate crowdworkers performance and to get a baseline understanding of what labeling this dataset looks like. In study 2, the authors investigate the potential of using crowd workers to perform the labeling task. They evaluate their performance on two levels of labeling accuracy:

• Image level: tests for the presence or absence of the correct label in an image 
• Pixel level: examines the pixel level accuracies of the provided labels

They show that AMT workers are capable of finding accessibility problems with an accuracy of 80.6 % and determining the correct problem type with an accuracy of 78.3%. They get better results when using majority voting as a labeling technique 86.9% and 83.9% respectively. They collected 13,379 labels, 19,189verification  labels from 402 workers. Their findings suggest that crowdsourcing both the labeling task and the verification task leads to a better quality result.

Reflection

The authors have selected experts in the paper as wheelchair users, when in real life they’re civil engineers. I wonder how that would have changed their labels/results. Since accessibility in the street is not only for wheelchair users. It’s worth investigating by using a pool of multiple experts. 

I also think that selecting the dataset of photos to work on was a requirement for this labeling system, else it would have been tedious amount of work on “bad” images. I can’t imagine how this would be a scalable system on google street view as a whole. The dataset requires refinement to be able to label.

In addition, the focal point of the camera was not considered and reduces the scalability of the project. Even though the authors suggest a solution of installing a camera angled towards sidewalks, until that is implemented, I don’t see how this model could work well in the real world (not a controlled experiment).

Discussion

• What are improvements that the authors could have done to their analysis?
• How would their labeling system work for random Google street view photos?
• How would the focal point of the GSV camera affect the labeling? 
• If cameras were angled towards sidewalks, and we were able to get a huge amount of photos for analysis, what would be a good way to implement this project?

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In this paper, the authors focused on the mechanism that using untrained crowdworkers to find and label accessibility problems in Google Street View imagery. They provide the workers images from Google Street View imagery to let them find, label and access sidewalk accessibility problems. They compared the results of this labeling task completed by six dedicated labelers including three wheelchair users and by MTurk workers. The comparison shows that the crowdworkers can determining the presence of an accessibility problem with high accuracy, which means this mechanism is promising about sidewalk accessibility. However, that mechanism still have problems such as locating the GSV camera in geographic space and selecting an optimal viewpoint, sidewalk width problem and the age of the images. In the experiments, the workers cannot label some of the images due to camera position, and the images may be captured three years ago. Additionally, there is no method to measure the width of the sidewalk, which is a need by the wheelchair users.

Reflections:

The authors combined the Google Street View imagery and MTurk Crowdsourcing to build a system which can detect accessibility challenges. This kind of hybrid system has a high accuracy in the finding and labeling of such kind of accessibility challenges. If this system can be used practically, the disables will benefit a lot with the help of the system.

However, there is some problems in the system. As is mentioned in the paper, the images in the Google Street View are old. Some of the images may be captured years ago. If the detection is based on these pictures, some new access problems will be detected. For this problem, I have a rough idea about letting the users of the system to update the image library. When they found some difference between the images from library and practical sidewalk, they can upload the latest pictures captured by them. As a result, other users will not suffer from the images’ age problem. However, this solution will change the whole system. Google Street View imagery requires professional capture devices which is not available to most of the users. As a result, the Google Street View will not update its imagery using the photos captured by the users, and the system cannot update itself using the imagery. Instead, the system has to build its own image library, which is totally different from the introduced system in the paper. Additionally, the photos provided by the users may be with low resolution, and it will be difficult for the MTurk workers to label the accessibility challenges.

Similarly, the problem that the workers cannot measure the width of the sidewalk can be solved if users can upload the width when they are using the system. However, it still faces the problem of lacking an own database and the system needs to be modified hugely.

Instead of detecting accessibility challenges, I think the system is more useful in tracking and labeling bike lanes. Compared with the accessibility of sidewalk, to detect the existence of bike lanes will suffer less from the age problem, because even the bike lanes were built years ago, they can still work. Also, there is no need to measure the width of the lanes, as all the lanes should have enough space for bikes to pass.

Question:

Is there any approach to solve the age problem, camera point problem and measuring width problem in the system?

What do you think about applying such a system to track and label bike lanes?

What other kinds of street detection problems can this system being applied to?

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SUMMARY

The authors of this paper aim to investigate the feasibility of recruiting MTurk workers to label and assess sidewalk accessibility problems as can be viewed by making use of Google Street View. The authors conducted two studies, the first, with 6 people (3 from their team of researchers and 3 wheelchair users) and the second, that investigated the performance of turkers. The authors created an interactive labeling interface as well as a validation interface (to help users to accept/reject previous labels).  The authors proposed different levels of annotation correctness comprising of two spectra – localization spectrum which includes image level and pixel level granularity and specificity spectrum which includes the amount of information evaluated for each label. They defined image-level correctness in terms of accuracy, precision, recall, and f-measure. In order to computer inter-rater agreement at the image-level, they utilized Fleiss’ kappa. In order to evaluate the more challenging pixel-level agreement, they aimed to verify the labeling by indicating that pixel-level overlap was greater between labelers on the same image versus across different images. The authors used the labels produced from Study 1 as the ground truth dataset to evaluate turker performance. The authors also proposed two quality control approaches – filtering turkers based on a threshold of performance and filtering labels based on crowdsourced validations.

REFLECTION

I really liked the motivation of this paper especially given the large number of people that have physical disabilities. I am very interested to know how something like this would extend to other countries such as India as it would greatly aid people with physical disabilities over there since there are many places with poor walking surfaces and do not have support for wheelchairs. I think that having such a system in place in India would definitely help disabled people be better informed about places that can be visited.

I also liked the quality control mechanisms of filtering tuckers and filtering labels since these appear to be good ways to improve the overall quality of the labels obtained. I thought it was interesting that the performance of the system improved with tucker count but the gains diminished in magnitude as the group size grew. I thought that the design of the labelling and verification interface was good and that it made it easy for users to perform their tasks.

QUESTIONS

1. As indicated in the limitations section, this work ‘ignored practical aspects such as locating the GSV camera in geographical space and selecting an optimal viewpoint’. Has any follow-up study been performed that takes into account these physical aspects? How complex would it be to conduct such a study?
2. The authors mention that image quality can be poor in some cases due to a variety of factors. How much of an impact would this cause to the task at hand? Which labels would have been most affected if the image quality was very poor?
3. The validation of labels was performed by crowd workers via the verification interface. Would there have been any change in the results obtained if experts had been used for the validation of labels instead of crowd workers (since they may have been able to identify more errors in the labels as compared to normal crowd workers)?

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Summary

Hara et al.’s paper “Combining Crowdsourcing and Google Street View to Identify Street-level Accessibility Problems” explores the crowdsourcing approach to locate and assess sidewalk accessibility issues by labeling Google Street View (GSV) imagery. Traditional approaches for sidewalk assessment relies on street audits which are very labor intensive and expensive or by reporting calls from citizens. The researchers propose using their designed interactive user interface as an alternative to proactively deal with this issue. Specifically, they investigates the viability of the labeling sidewalk issues amongst two groups of diligent and motivated labelers (Study 1) and then explores the potential of relying on crowd workers to perform this labeling task and evaluate performance at different levels of labeling accuracy (Study 2). By investigating the viability of labeling across two groups (three members of the research team and three wheelchair users), the results of study 1 is used to provide ground truth labels to evaluate crowd workers performance and to get a baseline understanding of what labeling this dataset looks like. Study 2 explores the potential of using crowd workers to perform the labeling task. Their performance is evaluated on both image and pixel levels of labeling accuracy. The findings suggest that it is feasible to use crowdsourcing for the labeling and verification tasks, which leads to the final result of better quality.

Reflection

Overall, this paper proposes an interesting approach for sidewalk assessment. What I think most is how feasible we can use that to deal with real-world issues. In the scenario studied by the researchers, the sidewalk under poor condition has severe problems and relates to a larger accessibility issue of urban space. The proposed crowdsourcing approach is novel. However, if we take a close look at the data source, we may question to what extent it can facilitate the assessment in real-time. It seems impossible to update the Google Street View (GSV) imagery on a daily basis. The image sources are historical instead the ones that can reflect the current conditions of the road sidewalks. 

I think the image quality may be another big problem in this approach. Firstly, the resolution of the GSV imagery is comparatively low and sometimes under poor light conditions, which is challenging to let the crowd workers make the correct judgement. There is possibility to use some existing machine learning models to enhance the image quality via increasing its resolution or adjusting the brightness. That could be a potential place to introduce the assistance of machine learning algorithms to achieve better results in the task.

In addition, the focal point of the camera was another issue which may reduce the scalability of the project. The CSV imagery is not collected merely for the sidewalk accessibility assessment, which would usually contain a lot of noises (e.g. block objects). It would be interesting to conduct a study about how much percent of the GSV imagery is of good quality in regards to the sidewalk assessment task.

Discussion

I think the following questions are worthy of further discussion.

• Are there any other important accessible issues existing but not considered in the study?
• What are improvements you can think about the authors could improve their analysis?
• What other potential human performance tasks can be explored by incorporating street view images?
• How effective do you think this approach can deal with the urgent real-world problems?

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Summary

In this paper, the authors discuss a crowd-sourcing method utilizing Amazon MT workers to identify accessibility issues in google street view images. They utilize two levels of street views for annotations: image-level and pixel-level. They evaluate intra and inter-annotator agreement and conclude a feasible level of accuracy of 81% (increased to 93% with minor quality control additions) for real-world scenarios.

The authors initiate the paper with a discussion about the necessity of such approaches. The solution could lead to more accessibility-aware solutions. The paper utilizes precision, recall and f1-score to consolidate and evaluate image-level annotations. For pixel-level annotations, the authors utilize two sets of evaluation metrics: overlap between annotated pixels and precision-recall scores. The experiments depict an inter-annotator agreement that makes the system feasible in real-world scenarios. The authors also utilize majority voting between annotators to improve the accuracy further.  

Reflection

The paper introduces an interesting approach to utilize crowd-sourced annotations for static image databases. This leads me to question other cheaper sources of images that can be utilized for this purpose. For example, google maps provides a more frequently updated set of images. Also, acquiring these images is more cost-effective. I think this will be a better alternative to the street-view images.

Additionally, the paper adopts majority voting to improve its results. Theoretically, this should lead to perfect accuracy. The method gets 93% accuracy after the addition. I would like to see examples where the method fails. This will enable development of better collation strategies in the future. I understand that in some cases, the image might be too unclear. However, examples of such failures would give us more data to improve the strategies.

Also, the images contain much more data than currently being collected. We can build an interpretable representation of such images that collect all world information contained in the images. However, the computational effectiveness and validity is still questionable. But, if we are able to better information systems, such representations might enable a huge leap forward in the AI research (similar to ImageNet). We can also combine this data to build a profile of any place such that it helps any user that wants to access it in the future (e.g.; accessibility of restaurants or schools). Furthermore, given the time-sensitivity of accessibility, I think a dynamic model will be better than the proposed static approach. However, this will require a cheaper method of acquiring street-view data. Hence, we need to look for alternative sources of data that may provide comparable performance while limiting the expenses.

Questions

1. What is the generalization of this method? Can this be applied to any static image database? The paper focuses on accessibility issues. Can this be extended to other issues such as road repairs and emergency lane systems?
2. Street view data collection requires significant effort and is also expensive. Could we utilize Google maps to achieve reasonable results? What is a possible limitation to applying the same approach on Google satellite imagery?
3. What about the time sensitivity of the approach? How will it track real-time changes to the system? Does this approach require constant monitoring?
4. The images contain much more information. How can we exploit it? Can we use it to detect infrastructural issues with government services such as parks, schools, roads etc.? 

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