Sample queries to test Drone Video Sensing Pipeline:

1. Bounding box: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given an image and a question about locating or describing an object, give its bounding box. Return only the bounding box coordinates in the format: <bbox>[[x, y, w, h],[x, y, w, h]...]</bbox> with the point of reference as the bottom left corner of the image. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. Give the bounding box for the green street crossing sign for bicycles at a street intersection.

b. Give the bounding box for the only red car in the image.

c. Give the bounding box for a building with circular roof structure.

d. Give the bounding box for a parking lot with available space.

e. Give the bounding box for a red car in this sequence of images.

f. Give the bounding box for a roof with solar panels in this image.

2. Color: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and a multiple-choice question about an object, select the best answer. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. What color is the largest paved motor road in the given image? A. dark brown, B. tan. C. dark gray, D. black

b. What color is the car in the center of the image? A. Red B. White C. Black D. Green

c. What color is the building dividing the street? A. Blue. B. Teal. C. Patina. D. Green.

d. What color is the most common among the cars in the top storey of this parking lot? A. Red B. White. C. Black D. Green

e. What color is the dedicated lane for bicycles in this image? A. Blue. B. Black. C. White. D. Green

f. What color are the windows of this multi-storeyed building in the bottom left of this image? A. Black B. Blue C. Brown D. Green

3. Counting: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and an object, count the number of objects in the scenes. Return only the count in this format: {number}. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. How many cars are there in this image?

b. How many buildings with circular roof structure?

c. How many available parking spaces are there in the parking lot on the right side of the image?

d. How many trees are there in this image?

e. How many cars are crossing the street intersections in this image?

f. How many pedestrians are in this image?

4. Distance: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and an object, count the number of objects in the scenes. Return only the count in this format: {number}. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. Which is farthest from me: tree, building, car, sedan, parking lot, street crossing?

b. Which is closest to me: tree, building, car, sedan, parking lot, street crossing?

c. which is closer to the building with a circular roof structure: parking lot, street splitting?

d. Which is closer to me: river, street intersection, parking lot, trees?

e. Which is closer to me: building with red roof or building with green roof?

f. Which is bigger: the park with trees or the building next to it?

5. Free space: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and the location near an object in the scene, indicate a free space region as a set of (x,y) pixel co-ordinates with the bottom left of the scene as the point of reference. Return this list of co-ordinates. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. Find the free space on the roofs of buildings that do not have any structures.

b. Find the free space for parking a car in a parking lot.

c. Find the free space for parking a sedate along the street curb.

d. Find the free space for parking a large semitrailer.

e. Find the free space in the lot occupied by a building with a hollow circular structure protruding from the roof.

f. Find the free space along the direction of traffic at the street split by a building with circular dome.

6. Function:(Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and the location near an object in the scene or a set of (x,y) pixel co-ordinates with the bottom left of the scene as the point of reference, indicate its function or purpose. Do not include reasoning or ask the user for more information.

Queries:

a. What is the ground between buildings on the left side of the road useful for?

b. What is the purpose of the green lane between the road and curb?

c. What is the purpose of the large white objects parked by the side of the road?

d. What is the nearest shelter for a pedestrian crossing the street when it rains?

e. What is the object in the scene that indicates the proximity of a social and commercial place such as a market or mall?

f. What are some of the co-ordinates in the scene where traffic can arrive at a body of water?

7. Height:(Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and the description of an objects in the scene or a set of (x,y) pixel co-ordinates with the bottom left of the scene as the point of reference, determine the object with the relative elevation that matches the query. Do not include reasoning or ask the user for more information.

Queries:

a. which is higher between the river and the car on the road adjacent to the river?

b. which is higher between the building with the solar panels on the roof or the building to the leftmost of it?

c. which is higher between the building roof and the tree tops next to it?

d. which is lower between the object at the bottom right of the scene or the park next to it?

e. which is lower between the following object categories: river, street, vehicles, buildings and trees?

f. which is higher between the buildings on the left of the scene and the buildings on the right of the scene?

8. Landing: You are a UAV (drone) landing safety advisor analyzing a low-altitude aerial image. Provide a comprehensive landing safety assessment in the following JSON format with key landing_feasibility as one of SAFE or CAUTION or UNSAFE, a numerical confidence score between 1 and 100, a list of hazards with each hazard having level such as low, medium or high, location such as one of four quadrants of the scene and reason for the hazard. You may also include recommendations in the json. Do not ask the user for more information.

Queries:

a. Is it safe to land in the park between the buildings?

b. Is it safe to land on the roof top of the largest building in the image?

c. Is it safe to land on the street next to river?

d. Is it safe to land in the parking lot?

e. Is it safe to land in the park by the side of the river?

f. Is it safe to land on the street intersection?

9. Captions: You are an aerial drone image analyst. Describe the scene provided and elaborate on the objects detected and their spatial relationships. If there are multiple images of the same scene, describe the temporal changes to the scene.

Queries:

a. Image showing a building with a circular roof structure and a split in the road

b. Image showing an empty road beside a river

c. Image showing a large parking lot between an enclave of buildings

d. Images following the traffic around a bend of the city streets

e. Images from a flyover a park with trees between buildings

f. Images showing buildings of different elevations in a small block.

10. Pointing: You are an aerial drone image analyst. Given the scene and some objects, point to areas of interest with a list of bounding box co-ordinates pertaining to the query using the bottom-left of the image as reference and in the format (x,y,w,h). Do not ask the user for more information.

Queries:

a. Locate all the roofs of buildings that are occupied by stationary structures.

b. Locate all the multi-storey buildings that are greater than three storeys high.

c. Locate a parking garage and the available spaces on it.

d. Locate the empty spots for cars to park between buildings but not on streets.

e. Locate safe play areas for children where there is little or no traffic.

f. Locate the highest point in the scene that is safe to land.

11: Uncommon: You are an aerial drone image analyst. Given the scene and some objects, identify the object accurately at the location given in the query using the bottom-left of the image as reference and in the format (x,y) even if the object is uncommon. Do not ask the user for more information.

Queries:

a. Identify the object at location (132,235)

b. Identify the object at location (0,15)

c. Identify the object at location (450,80)

d. Identify the object at location (750,1025)

e. Identify the object at location (20,545)

f. Identify the object at location (225,235)

12. Spatial: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are an aerial drone image analyst. Given a scene as an image and a multiple-choice question about spatial relationships, select the best answer. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. What direction is the parking garage in from the buildings on the right side of the road assuming the top of the image is North? A. North, B. East. C. South, D. West

b. Where is the park from the street? A. Left B. Right C. Above D. Below

c. Where is the multi-storey building in the image? A. top-Left B. top-Right C. bottom-left D. bottom-right

d. Which direction would rainfall flow towards when falling on the dome of the building splitting the street? A. Left B. Right C. Above D. Below

e. Where is the sun given the shadows of the trees in the park? A. Left B. Right C. Above D. Below

f. Where is the object whose shadow is seen in the top-left of the scene? A. above or below the scene B. inside the scene C. Left or right of the scene.

#codingexercise: CodingExercise-12-14-2025.docx

 This is a summary of the book written by “The Scaling Value Playbook: A practical guide for creating innovation networks for impact and growth (De Gruyter Business Playbooks)” written by John Bessant and Ian Gray and published by De Gruyter in 2024. Innovators must build a strategic network to understand how value is created and scaled. The authors shows us how to map out and navigate relationships with consumers, competitors and others who can help or hinder our innovation’s adoption. Infographics, sidebars and exercises in this book help to drive home the message.

The authors begin by dispelling the myth of the “overnight success.” Icons like Steve Jobs, Richard Branson, and James Dyson have all remarked that what appears sudden is, in truth, the result of years of hard work. Consider the story of Otto Rohwedder, who invented the bread-slicing machine in the early 1900s. Rohwedder faced financial setbacks, a devastating factory fire, and skepticism from potential users. Only after years of persistence did his invention find commercial success, leading to a dramatic surge in bread sales and, eventually, transforming the way Americans consumed bread.

This anecdote illustrates a central theme of the book: an idea alone is rarely enough to scale. Instead, innovators must build systems and strategies that support their vision. The process of testing, refining, and launching new ideas is just the beginning. The real challenge lies in the “messy middle”—the uncertain, often perilous stages where many innovations falter. As Professor Rosabeth Moss Kanter notes, long-term success comes to those who approach scaling strategically, leveraging networks of mentors, partners, and diverse teams to navigate obstacles and seize opportunities.

Evidence is another cornerstone of successful scaling. Moving from a handful of early adopters to mainstream acceptance requires proof—both objective and emotional. Some stakeholders, such as those in the pharmaceutical industry, demand rigorous data before embracing a new product. Others are swayed by compelling stories and endorsements. The authors advise innovators to assess what kind of evidence their audience needs and to gather testimonials from experts, customers, and influencers. When people see that others trust and use an innovation, they are more likely to adopt it themselves.

The story of Earl Tupper and Brownie Wise exemplifies this principle. Tupper’s invention of airtight plastic containers initially struggled in the marketplace, despite positive media coverage. It was Wise’s innovative sales strategy—hosting home parties where women could see and use the product—that provided the social proof needed to drive adoption. Today, Tupperware is a global brand, a testament to the power of evidence and network-driven growth.

Scaling innovation also requires a clear vision and an understanding of the barriers ahead. The authors introduce the concept of the “scale panorama,” a three-part framework for planning growth. Innovators must articulate their long-term vision, define the value they aim to create, and specify tangible outcomes such as market share or the number of people served. They must also identify their target audience—whether regional, national, or global—and map out the route to scale, including critical success factors and potential obstacles. Setting short-term goals, achievable within one to three years, helps maintain momentum and focus.

No innovation scales in isolation. Building a network of partnerships is essential. The authors draw parallels to historic achievements, like the ascent of Mount Everest, which succeeded only through the coordinated efforts of a dedicated team. Scaling innovation similarly depends on cultivating relationships with a diverse ecosystem of contributors.

To help innovators understand their networks, the book introduces the “Nine Cs”—distinct roles that people and organizations play:

• Creators generate new value.

• Consumers use products or services.

• Captors (investors) capture value.

• Channels deliver products to consumers.

• Conveyors add value through components or expertise.

• Coordinators bring together various elements.

• Cartographers (regulators, policymakers) shape the environment.

• Competitors vie for resources and customers.

• Complementors provide supporting products or services.

Entities may play multiple roles, and these roles can shift over time. For example, Lebanon’s Ministry of Education acted as a regulator, consumer, and conveyor in its partnership with the education innovator QTL.

The book encourages innovators to regularly assess their relationships within this network, rating their confidence in each area and identifying gaps. Companies like Nike have evolved from creators to coordinators, relying on partners to expand their reach. Liverpool Football Club’s fans serve as both complementors and unofficial cartographers, influencing the club’s decisions and promoting its brand.

Scaling requires organizational agility. Innovators must choose models—franchising, licensing, open source—that suit their goals and be prepared to adapt. Impact Hub’s shift from a centralized to a distributed support model enabled rapid expansion. Limiting bureaucracy and empowering staff, as Netflix does, fosters flexibility and responsiveness. Netflix’s evolution from distributor to creator, conveyor, complementor, and cartographer demonstrates the dynamic nature of scaling in practice.

 While on-board vision LLMs gain incredible capability by training on relevant datasets, the cost-effectiveness of the agentic retrieval system must be comprehensive to cover all commercial analytics query such as the following:

1. Agriculture

Drone data in agriculture is primarily used for precision farming and crop management.

• "Generate multispectral imagery of a specific field section to detect early signs of crop stress and disease." 1

• "Process RGB optical images to create a high-resolution orthomosaic map for identifying weed locations and optimizing pesticide application."2

• "Analyze a series of time-lapsed drone flights to track crop growth and development over the season for yield prediction." 3 4

2. Construction and Infrastructure

Drones in this sector are used for surveying, monitoring progress, and conducting safety inspections of hard-to-reach areas.

• "Capture high-resolution aerial imagery of a construction site to create a 3D model for progress monitoring and comparison with the original blueprint."

• "Perform a thermal imaging scan of a building's roof to identify heat anomalies or insulation defects."

• "Conduct an automated visual inspection of a bridge using an optimized flight path to detect structural defects like cracks or corrosion."

3. Mining

Mining operations use drones for surveying, volume calculation, and site safety.

• "Process drone data to calculate the exact volume of stockpiles (e.g., coal, ore) for inventory management."

• "Generate a high-precision topographic map of the mine pit using LiDAR data to assess terrain changes and plan future excavation."

• "Capture real-time video footage of blasting zones to ensure the area is clear of personnel before detonation and to assess post-blast results."

4. Energy

The energy sector benefits from drones for the inspection and maintenance of critical infrastructure like solar farms, wind turbines, and power lines.

• "Conduct a thermal inspection of a solar panel array to identify non-functioning or overheating panels that require maintenance."

• "Capture high-resolution images and video of wind turbine blades to check for micro-cracks or erosion without requiring manual climbing inspections."

• "Perform an environmental survey of potential renewable energy sites (e.g., for a new solar project) to map sensitive habitats and plan appropriate setbacks."

5. Environmental Monitoring

Drones are used to monitor environmental health, track changes over time, and help with conservation efforts.

• "Generate multispectral or hyperspectral data to assess the health of a forest area affected by drought or deforestation."

• "Capture aerial imagery to map the extent of an oil spill or a flood, aiding in disaster response and damage assessment."

• "Perform a survey of a coastal area to monitor shoreline erosion or coral reef health over time using detailed imagery and bathymetry maps."

6. Urban Planning

Drones provide city planners with high-resolution imagery and 3D models for informed decision-making.

• "Generate a 3D city model from drone photogrammetry for urban development visualization and impact assessment."

• "Map all rooftops within a designated area using high-resolution imagery to track construction density and identify unauthorized modifications."

• "Conduct a rapid aerial survey to create a high-precision orthomosaic map that can be integrated into existing GIS software for infrastructure planning and management."

 Drone Analytics Benchmarks

Drone video pipelines are evaluated not only by model-specific metrics (like mAP) but also by system-level performance indicators that span video quality, throughput, latency, scalability, and mission effectiveness. Research studies and commercial whitepapers highlight metrics such as frame alignment accuracy, bitrate efficiency, latency-to-decision, operator situational awareness scores, and mission coverage ratios. These provide a holistic view of drone video sensing pipelines across diverse applications (traffic monitoring, agriculture, surveillance, emergency response).

System-Level Performance Metrics for Drone Video Pipelines

Metric Category Specific Metrics Context / Source

Video Quality & Compression PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), BD-Rate (bitrate savings), RGB-PSNR UAVBench video compression benchmarks show ~20% BD-Rate savings with learned codecs 1

Detection & Tracking mAP (mean Average Precision), MOTA (Multiple Object Tracking Accuracy), ID switches, IoU (Intersection over Union) UAVDT & VisDrone benchmarks; UAVBench tracking protocols

Latency & Throughput End-to-end pipeline latency (ms/frame), FPS throughput, decision latency SkyQuery platform emphasizes frame alignment speed and query execution latency 2

Situational Awareness & Coverage Mission coverage ratio (area/time), operator video quality scores, trajectory optimization efficiency ALPHONSE project metrics for drone operator training 3

Scalability & Robustness Multi-agent coordination efficiency, resilience to occlusion/motion blur, adaptability to weather/altitude UAVBench spatial intelligence benchmarks; commercial Drone Industry Insights reports

Operational Effectiveness Task completion rate, hazard detection accuracy, resource utilization (battery, bandwidth) SkyQuery case studies in traffic hazard detection and pedestrian mapping

The trend graphs that are popular in this domain include:

• Rate–Distortion Curves (BD-Rate vs. PSNR): UAVBench shows learned codecs outperform HEVC in outdoor UAV footage but underperform in indoor fish-eye scenes.

• Latency vs. Accuracy Trade-offs: SkyQuery demonstrates that faster frame alignment reduces latency but slightly lowers detection accuracy.

• Coverage vs. Time Graphs: ALPHONSE project visualizes drone trajectory optimization, showing improved coverage ratios with optimized flight paths.

• Scalability Trends: Drone Industry Insights reports highlight increasing demand for real-time video analytics at scale, with commercial pipelines trending toward edge-cloud hybrid architectures.

But with the usual caveats that:

• Domain Shift: Metrics like BD-Rate savings collapse under fish-eye distortion or night UAV footage.

• Latency Bottlenecks: Even high-mAP models fail operationally if pipeline latency exceeds ~200 ms/frame in real-time UAV monitoring.

• Operator-Centric Metrics: Whitepapers stress that situational awareness and mission coverage are as critical as technical accuracy.

• Commercial Bias: Industry reports (DroneII) emphasize market growth and scalability but may underreport technical limitations.

Existing versus Proposed Benchmark

UAVBench1 (1) comes close to fulfilling most of the above criteria but it mandates that the analytics be packaged as vision-LLMs (VLM) where as Drone-Analytics-Benchmark as proposed (2) is more flexible.

References:

1. https://www.emergentmind.com/topics/uavbench

2. https://github.com/ravibeta/ezbenchmark or https://1drv.ms/w/c/d609fb70e39b65c8/IQB7nJEDVKaPTKMMUlkpHmgPAZtX_gA7k2j3_xqzjyygTD4?e=ecVoMr

3. https://dspace.mit.edu/bitstream/handle/1721.1/143775/3486607.3486750.pdf?sequence=2

4. https://www.thinkmind.org/articles/icas_2022_1_40_20018.pdf

#codingexercise:

Codingexercise-12-11-2025.docx

 This is a summary of a book titled “Binge Times: Inside Hollywood’s furious billion-dollar battle to take down Netflix” written by Dade Hayes and Dawn Chmielewski and published by Wiliam Morrow in 2022. As Netflix moves to acquire Warner Bros. this looks at how it grew. Netflix had already positioned itself as a leader in streaming when pandemic struck. As the streaming industry boomed and brought more players including traditional media companies into a fierce competition, Netflix’s business became so mainstream that only Disney posed a significant threat.

In the mid-1990s, the seeds of a revolution in entertainment were quietly being sown. Streaming, as we know it today, had its humble beginnings in 1996, when visionaries like Jonathan Taplin launched Intertainer, a service that delivered movies to computers via the internet. Back then, the idea of watching a film online seemed almost fantastical—computers were slow, the internet was new, and streaming a movie required both technical ingenuity and a leap of faith. Yet, as high-speed internet became more widespread, the groundwork was laid for a new era in media consumption.

Amid this technological ferment, Netflix emerged—not as the streaming giant we know today, but as a mail-order DVD rental service. Subscribers paid a monthly fee to receive DVDs by mail, a model that quickly gained a devoted following. Netflix’s innovation didn’t stop there: in 1999, it introduced a system allowing customers to keep DVDs as long as they wanted, and soon after, a recommendation engine that harnessed user ratings to personalize suggestions. This data-driven approach would become a hallmark of Netflix’s strategy.

By 2007, with over half of American households enjoying broadband internet, Netflix recognized the time was ripe to move online. The company launched its first streaming service, Watch Now, which, though technically imperfect, marked a pivotal shift. The success of YouTube had already shown that audiences craved on-demand content, and Netflix was determined to deliver. Hollywood, wary of piracy after witnessing the upheaval in the music industry, hesitated, but Netflix pressed forward.

The true turning point came in 2013, when Netflix debuted its first original series, House of Cards. The company made a bold $100 million bet on 26 episodes, signaling its commitment to original content. The entire first season was released at once, inviting viewers to binge-watch—a radical departure from traditional television’s weekly release schedule. This strategy not only captivated audiences but also set a new industry standard. Netflix’s global reach meant that viewers around the world could watch the same shows simultaneously, transforming it into the first truly global television network.

As Netflix soared, traditional media companies scrambled to adapt. HBO, once a pioneer in premium cable, struggled to transition to streaming. Despite the storied history of groundbreaking shows like The Sopranos and Game of Thrones, HBO’s streaming ventures, such as HBO Now and HBO Max, were hampered by legacy contracts and late launches. Meanwhile, Apple entered the fray, leveraging its vast ecosystem of devices. By 2019, Apple’s services business, including streaming, was generating more revenue than its iconic Mac computers and iPads. Yet, Apple TV+ stumbled out of the gate, revealing the challenges tech companies face when entering the world of entertainment.

Disney, too, underwent a profound transformation. Under CEO Bob Iger, Disney recognized that the era of the traditional media conglomerate was ending. The company invested heavily in high-quality content, acquiring Marvel and Lucasfilm, and eventually launched Disney+, which rapidly amassed over 120 million subscribers in more than 50 countries. Disney’s approach was to focus on strong brands and global reach, but it still faced limitations as it remained primarily an exporter of American content.

Amazon, with its acquisition of MGM in 2021, further intensified the competition. Amazon Prime Video, initially an add-on to its retail membership, began to carve out its own identity with original films and series. The MGM deal brought a vast library of movies and iconic franchises under Amazon’s umbrella, fulfilling CEO Jeff Bezos’s ambition for a global entertainment powerhouse.

The COVID-19 pandemic in 2020 accelerated these trends. As lockdowns forced people indoors, streaming became not just a diversion but a lifeline. Netflix, already entrenched as the default streaming service, saw its influence grow even further. The global success of shows like Squid Game, which topped charts in 90 countries, underscored Netflix’s dominance, and the shift toward international, multilingual content. Between 2019 and 2021, the number of Americans streaming Netflix in languages other than English soared by 71%.

By 2022, Disney stood as the only real challenger to Netflix’s supremacy. Other competitors, like Apple TV+ and HBO Max, faced cultural and contractual hurdles. The old Hollywood model of exporting domestic titles was fading, replaced by a new paradigm of global, on-demand entertainment. Despite predictions that Netflix would be overtaken, it remained at the forefront, continually adapting and thriving in an industry defined by relentless change.

“Binge Times” thus chronicles not just the rise of Netflix, but the seismic shifts that have reshaped Hollywood and the way the world watches television. It is a story of innovation, disruption, and the fierce battles waged in pursuit of viewers’ attention in the streaming age.

#continuation of yesterday's article: https://github.com/ravibeta/ezbenchmark/ 

 TPC-H for aerial drone image analytics

This is a proposal for domain-adapted benchmark by taking the TPC-H v3 decision support queries which stress-test OLAP systems with business oriented data warehouse workloads) and reframes them for aerial drone image analytics. This would create a standardized way to evaluate drone video/image pipelines with SQL-like queries, but grounded in geospatial and vision tasks.

Step 1. Schema adaptation:

TPC-H schema has tables like CUSTOMER, ORDERS, LINEITEM. For drone imagery, we’d define analogous tables:

IMAGE: metadata for aerial images (id, timestamp, location, altitude, sensor type).

OBJECT_DETECTION: detected objects (image_id, object_type, bounding_box, orientation, confidence).

TRACKING: temporal sequences (track_id, object_id, trajectory, speed, direction).

EVENTS: higher-level events (traffic jam, unauthorized entry, wildfire hotspot).

REGIONS: geospatial polygons (urban, rural, restricted zones).

Step 2. Query adaptation:

The following table lists the adaptations:

TPC-H Query Original Purpose Drone Analytics Adaptation

Q1: Pricing Summary Report Aggregate line items by date Detection Summary Report: Count objects per type per region per day (e.g., vehicles, aircraft).

Q3: Shipping Priority Orders with high priority Event Priority: Identify urgent drone-detected events (e.g., accidents, intrusions) sorted by severity.

Q5: Local Supplier Volume Join across regions Regional Object Volume: Join detections with regions to compute density of vehicles/people per zone.

Q7: Volume Shipping Compare nations Cross-Region Traffic Flow: Compare object counts across multiple geospatial regions over time.

Q8: Market Share Share of supplier Model Share: Compare detection accuracy share between different drone models or sensors.

Q9: Product Profit Profit by supplier Event Cost Impact: Estimate resource usage (battery, bandwidth) per event type.

Q10: Top Customers Identify top customers Top Hotspots: Identify top regions with highest frequency of detected anomalies.

Q12: Shipping Modes Distribution by mode Flight Modes: Distribution of detections by drone altitude or flight mode.

Q13: Customer Distribution Count customers by orders Object Distribution: Count detections by object type (cars, pedestrians, aircraft).

Q15: Top Supplier Best supplier Top Detector: Identify best-performing detection algorithm (highest precision/recall).

Q18: Large Volume Customer Customers with large orders Large Volume Region: Regions with unusually high detection counts (e.g., traffic congestion).

Step 3. Metrics and Evaluations:

Just like TPC-H measures query response time, throughput, and power, the drone benchmark would measure:

Query Latency: Time to answer detection/tracking queries.

Throughput: Number of queries processed per minute across drone streams.

Accuracy Metrics: Precision, recall, mAP for detection queries.

Spatial-Temporal Efficiency: Ability to handle joins across time and geospatial regions.

Resource Utilization: CPU/GPU load, bandwidth usage, battery impact.

Step 4. Sample query:

This query evaluates object detection density per region per week, analogous to TPC-H’s line item aggregation:

SELECT

    region_id,

    object_type,

    COUNT(*) AS object_count,

    AVG(confidence) AS avg_confidence

FROM OBJECT_DETECTION od

JOIN REGIONS r ON od.location WITHIN r.polygon

WHERE od.timestamp BETWEEN '2025-12-01' AND '2025-12-07'

GROUP BY region_id, object_type

ORDER BY object_count DESC;

Future:

This benchmark is reproducible for drone analytics pipelines and provides standardization. Vendors can compare drone video systems and pipelines. It performs stress-testing using geo-spatial joins, temporal queries, and detection accuracy at scale. We could call it the Drone-Analytics Benchmark proposal.

References:

• Full Specification: https://1drv.ms/w/c/d609fb70e39b65c8/EXuckQNUpo9MowxSWSkeaA8Bm1f-ADuTaPf_GrOPLKBMPg?e=uoA10o

 This is a summary of a book titled “How to handle a crowd” written by Anika Gupta and published by Simon Element in 2020. In the world of online groups and communities, the decorum is established not by traditional authorities but by moderators who facilitate virtual cooperation and interaction. She studies a wide range of digital communities in her book. She says that these communities tend to foster partisan echo chambers that increase polarization and deliberate effort is required to nurture healthy debate. The role of moderator is time-consuming and might even be thankless but with preparation, they shape the way people build relationships today.

The author’s research spans a wide array of digital communities, revealing a recurring challenge: the tendency for these spaces to become partisan echo chambers, amplifying polarization rather than fostering understanding. She argues that nurturing healthy debate in such environments requires deliberate effort and thoughtful moderation. The work of a moderator, she notes, is often time-consuming and thankless, yet with preparation and vision, these individuals profoundly influence how relationships are built in the digital age.

Moderators—sometimes affectionately called “mods”—are the architects of community identity and the guardians of online discourse. Their responsibilities are multifaceted, ranging from short-term regulatory actions, like banning users who break the rules, to long-term strategies that shape the very nature of the conversations within a group. They act as digital hosts and storytellers, drawing on traditions as old as the bard, weaving narratives that help communities thrive. Many moderators are volunteers, driven by a shared sense of purpose and an ethos of care and support.

People join online communities to engage in conversations that resonate with their interests and identities. Sociologists Jenny Preece and Diane Maloney-Krichmar define an online community as a group united by common interests or purposes, interacting according to agreed-upon policies in a virtual setting. Yet, maintaining these communities is no small feat. Without traditional elders or authorities, the question arises: who enforces the rules and shapes the boundaries? The answer, Gupta suggests, lies with the moderators.

The book highlights the dangers of echo chambers, where polarization grows unchecked. Drawing on a Pew Research Center study, <Author>Gupta</Author> notes that Americans often find political discussions with those holding opposing views to be exasperating, with such exchanges frequently deepening divides rather than bridging them. This phenomenon, known as “affective polarization,” has been on the rise in the United States since 1988.

Yet, the narrative is not entirely bleak. Gupta shares examples of communities that have managed to foster healthy dialogue across divides. One such group is Make America Dinner Again (MADA), founded by Justine Lee and Tria Chang. MADA began as a face-to-face dinner party where liberals and conservatives could “break bread” and engage in meaningful conversation. Its success led to branches across the country and, eventually, an online presence on Facebook. MADA’s moderators focus on relationship-building, reaching out to members individually when rules are broken, and guiding them toward more respectful interactions. They limit how often members can comment on threads, encouraging listening and preventing a few voices from dominating. Over time, these efforts have cultivated a more civil and respectful tone, offering a blueprint for other groups seeking understanding.

The book also examines the complexities of moderating conversations about racial justice. Groups like Pantsuit Nation, formed to support Hillary Clinton’s presidential candidacy, faced criticism for centering white members’ experiences and mishandling race-related topics. In response, the group hired Grace Caldara as Director of Engagement, reduced the moderator team, and implemented training to manage tension and prioritize moderator self-care. Meanwhile, a spinoff group, Real Talk: WOC & Allies for Racial Justice and Anti-Oppression, requires members to commit to active antiracism and undergo allyship training, ensuring that participants have confronted their own biases before joining.

Moderating neighborhood groups presents their own challenges. Moderators help members navigate crises and form connections, but they must also contend with platform instability and the threat of fake accounts. For example, Peggy Robin, who manages a neighborhood LISTSERV in Washington, D.C., had to quickly migrate years of content when Yahoo Groups shut down. She also enforces a zero-tolerance policy against fake advertisers and impersonators.

Despite the difficulties, many moderators find the work rewarding. Christi Ketchum, founder of the Sacramento Sister Circle, sees herself as a “bridge builder,” mentoring young women and fostering financial independence within her community.

The book touches on the world of online gaming, where guild leaders and moderators play crucial roles in shaping community culture and responding to issues like discrimination and polarization. These leaders often create inclusive spaces and support members through mentorship, though the emotional labor can be exhausting.

The author concludes that while moderators cannot fix all the problems of online discourse, their influence is significant. They rely on foresight, technical skill, and conflict management to create spaces for civil conversation. Though most are unpaid, their work endures, shaping how people meet, work, play, and connect in the digital age.

#continuation of yesterday's article: VideoSensingBenchmarks.docx