If you follow the conversations here on edparsons.com, you know how deeply invested I am in the evolution of our field—whether we are talking about new paradigms in Location Intelligence, the latest standards emerging from the Open Geospatial Consortium, or the fascinating ways AI is integrating with geographical information systems. Geomob has always been one of the absolute best places to dive into these topics in person.

It’s an incredible gathering that bridges the gap between casual mapmakers and seasoned geospatial professionals. We’ve got a fantastic lineup of short talks for this edition:

• Steven Feldman – You CAN teach an old dog new tricks
  Really Steven !!!
• Justin Griffioen – Locatix
• Katy Alexander – Mapmakers of the subsurface
• Alastair Graham – Tidying up fields
• Luke Chittock – Linckia
• Matt Amos – Watering Holes of Inaccessibility

I’m thrilled to continue my support for this brilliant community. Having been a sponsor of Geomob for a number of years, I’ve seen firsthand how these evenings spark fresh ideas and drive innovation across WebGIS and spatial data analysis. It’s a privilege to help keep the momentum going and ensure the #geobeers keep flowing after the talks!

Whether you are building interactive web maps for fun, wrestling with complex spatial computing challenges, or just curious about the world of location data, you won’t want to miss it.

Come along, hear some great talks, and join the conversation. You can register for the event here.

See you at the Geovation Hub!

Aviation photography demands a brutal balancing act between technical precision and hostile environmental conditions. Photographers must wield heavy, expensive telephoto lenses to bridge the distance to subjects tearing through the sky at hundreds of miles per hour, requiring masterful panning techniques and lightning-fast autofocus to maintain sharpness.

Even with perfect tracking, jet exhaust and atmospheric heat haze frequently distort the air, turning meticulously composed frames into a blurry mess. Exposure presents another constant battle: metering a dark metallic fuselage against a brightly backlit sky often underexposes the aircraft, while finding the exact shutter speed to capture the rotational blur of a propeller—without introducing camera shake or freezing the blades into static, lifeless sticks—requires relentless trial and error.

Beyond the camera, simply navigating strict airport security perimeters and chain-link fences to find an unobstructed vantage point makes the logistical preparation as demanding as the shoot itself.

Aviation photography is both a hobby and a “side hustle” as a Professional Aviation Photographer, working on particular aircraft and event projects.

This summer I worked with the organisers of the bi-annual Farnborough International Air Show creating content that was used by their social media and sales teams in the week running up to the airshow itself.

This you tube short gives you come idea of the environment, it was both hot and noisy !

I had an interesting conversation on attending Farnborough with the “All the Gear podcast” which was fun but certainly made me feel my age remembering going along to the show as a teenager in the 1980’s ! If you are passionate about capturing aircraft, the “All the Gear Podcast” is an essential listen because it strips away the pretension often found in specialized photography communities. Hosted by four experienced aviation photographers—Dave, Keiran, Bradley, and Aled—the show delivers honest, “no gatekeeping, no fluff” discussions about the realities of the craft. Rather than just boasting about perfect shots, the hosts openly share the moments that don’t go to plan, offering relatable insights for both beginners and seasoned shooters.

I was also able to get in a few overseas trips over the summer visiting Air Shows in Poland and Sweden and visiting an operational Polish Air Force helicopter base to see some of the last Soviet era Mil Mi-24 Hind helicopters still in military service. Working with active service personnel is always fascinating, as they seem to be made of the same “stuff” wherever they come from and (shocking I know) whatever they fly..

Highlight of my summer was an Air to Air opportunity to photograph an F-15E of the USAF based at RAF Lakenheath over the Llŷn Peninsula of North Wales, a field trip destination I remember for my undergraduate student days. For an Aviation Photographer this is the ultimate type of trip, the opportunity to fly alongside a military aircraft from an “open door” aircraft is a rare and expensive undertaking, but is also highly addictive.

I still have a trip of attend Fleet Week in San Fransisco next month, so my photographic adventures are not finished yet for the year, but it will be strange arriving at SFO and not turning right to head down towards Mountain View !

If you are interested in more of my photography, take a look at photos.edparsons.com and should you be looking for a early Christmas Present in the form of Wall Art store.edparsons.com

As an advisor to the team at Geolytix, I recently had the pleasure of hosting a special series of podcasts diving deep into how AI is reshaping the world of spatial analytics. We tackled everything from predictive location modeling and machine learning applications to what the future actually looks like for GIS professionals trying to make sense of massive, complex datasets.

You can listen to the full series over on the Geolytix Blog.

Whether you are a seasoned location analyst or just curious about how algorithms are learning to read map patterns, there’s plenty in these conversations to sink your teeth into.

In fact, with summer in full swing, I have a modest proposal: forget the latest airport thriller or beach read. Nothing quite pairs with the soothing sound of crashing ocean waves, a cold drink, and sand between your toes like a deep-dive audio discussion on spatial machine learning algorithms and spatial data infrastructure.

Swap out the usual summer playlist, pack your headphones, and give them a listen on your next break. You might just return to the office with a whole new perspective on spatial intelligence.

During my time working at Google, we used to describe Google Earth and Maps as a mirror of the real world.

It wasn’t just a marketing slogan; it was a guiding principle. To us, the earth-observation and geospatial teams, the core value proposition of Google Earth was objective truth. It was a digital double of our planet built on photons bounced off satellites, stitched aerial photography, and meticulously modeled 3D geometry. If you zoomed into a street corner in Tokyo, a rural farm in Kansas, or Teddington High Street, you trusted that what you were looking at actually existed.

That absolute bedrock of trust was structurally shattered this week—briefly, thankfully, but dangerously—when Google rolled out (and subsequently had to reverse) its “Nano Banana 2” AI image generation feature directly inside Google Earth.

Yes, you read that right. A feature named after a fruit was unleashed to let users paint synthetic, hallucinated realities straight onto the planet’s trusted baseline layer.

The “Nano Banana” Blunder: When a Mirror Becomes a Magic Eye Poster

The pitch from the product team was dripping with standard Technical Product Manager buzzwords. They wanted to help users “visualise the past,” dream up real estate projects, or see what an empty lot might look like as a community garden.

Sounds harmless enough in a vacuum, right or on your desktop GIS system?

Except the internet immediately did what the internet always does when handed a frictionless tool for synthetic fabrication. Within hours of launch, researchers, OSINT investigators, and digital watchdogs began using Nano Banana 2 to drop fake refugee crises at borders, plant nuclear power plants in restricted zones, and simulate fatal crashes or geopolitical flashpoints on real streets.

Because the resulting screenshots maintained the authoritative interface of Google Earth, they could easily be weaponized as pristine disinformation. By July 31st yes that’s just yesterday !—barely 24 hours after shouting about how users could now render ancient Pompeii or futuristic sci-fi utopias—Google was forced to retreat, pulling the feature and admitting they needed to work on “stronger guardrails”.

How did a company that once deeply respected the sanctity of geospatial truth sign off on something so fundamentally reckless?

How did a company that once deeply respected the sanctity of geospatial truth sign off on something so fundamentally reckless?

The Perverse Mandate: “Add AI to Everything”

We are living through a corporate panic. In boardrooms across Silicon Valley, leadership is terrorized by the prospect of being left behind in the generative AI gold rush. The mandate from the top has devolved into a blunt directive: Add AI to everything, everywhere, immediately. I felt this at the end of my time at Google I can only imagine how much stronger this has become..

It doesn’t matter if the product calls for it. It doesn’t matter if the product’s entire historical equity is built on being an objective record of reality. If a product doesn’t have a generative text-to-image prompt box slapped onto it, senior leadership panics.

In their haste to check an AI box, the architects of this feature completely lost sight of what makes Google Earth valuable. Google Earth is not a canvas for creative writing; it is a reference library. You don’t want an AI “interpreting” or “reimagining” a map any more than you want an AI to creatively hallucinate your bank statement.

A Total Amnesia of “Geo” Values

OK this sounds like the rantings of someone who used to work for Google Geo of course, but I believe this blunder also points to a deeper cultural rot within the current configuration of the company: a severe lack of institutional memory.

When you cycle through staff, or when leaders who have no foundational background in Geo-products are handed the keys to legendary mapping infrastructure, you get a profound disconnect. New arrivals to the Geo team saw Google Earth as a static UI waiting to be “unlocked” by generative media. They treated it like a blank social media feed.

Veterans of the Geo organization understood an unwritten rule that took decades to build: Geospatial trust is hard to win and terrifyingly easy to lose. Google Earth is relied upon by human rights investigators, journalists, emergency responders, and courts of law as a baseline truth layer of our planet. Once you train the public to accept that the satellite view can be whimsically altered by typing a prompt about a “lakeside cabin” or a “futuristic metropolis”, you destroy the integrity of the platform. If anything on the screen can be AI-generated, then nothing on the screen can be trusted.

The Mirror Can Never Be Unbroken Entirely

Google’s quick rollback of Nano Banana 2 is a welcome relief, but the damage to the narrative is already done. The genie is out of the bottle. Bad actors have already captured screenshots, and the illusion of Google Earth as an trusted, objective mirror of the world has a permanent crack in it.

If Google wants to retain any remaining credibility for Google Earth, leadership needs to stop treating its core products as testbeds for whatever experimental model management wants to hype next.

Google remains a trustworthy company, however the technology deployed is not Google’s alone and there are other organisations who would use this capability for their own goals, this is a wake up call to the industry to be careful what you trust when it comes to Geospatial as much as Social Media these days.

keep the hallucinations out of the geography

Some things in this world should remain real. Leave the bananas in the fruit bowl, and keep the hallucinations out of the geography.

Who else got into geography to change the world rather than just digitise it?

If you read Rohan Silva’s recent essay in The Times, you’ll have seen him articulate a shift that is quietly rewiring the way we think about knowledge work. Silva captures a profound transition brought on by the rise of Artificial Intelligence: we are moving away from a world defined by mastering the how, and entering a world where value is entirely driven by the what.

Reading his piece, I couldn’t help but reflect on how perfectly this mirrors the evolution—and the immediate future—of our own geospatial industry.

For decades, to be a “Geographer” or a geospatial professional meant acting as a gatekeeper of the how. If a city planner, a climate scientist, or a logistics manager wanted to understand the spatial dynamics of their problem, they had to come to us. And our work was intensely mechanical. We spent our days wrangling shapefiles, fighting with map projections, writing complex SQL queries, and navigating the Byzantine user interfaces of desktop GIS software.

The barrier to entry for spatial analysis wasn’t the ability to think spatially; it was the ability to operate the machinery. We were defined by our technical execution.

AI is dismantling that barrier, and it is doing it faster than many in our industry realise, a point made forcefully by Peter Rabley at this year’s GeoIgnite.

With the advent of large language models and generative AI, the mechanics of spatial analysis are being abstracted away. We are rapidly approaching a point where a user can simply look at a map and ask, in plain English: “Show me the neighborhoods with the highest density of elderly residents that are also within a 100-year flood zone, and highlight the ones lacking accessible public transit.”

The AI will write the query, fetch the data, perform the intersection, and render the visualization. The machine handles the how.

For some in the geospatial community, this feels like an existential threat. If a machine can run a spatial buffer and render a heat map in three seconds based on a voice prompt, what is the role of the GIS analyst?

But as Silva’s essay implies, this isn’t the end of our profession—it is a massive elevation of it. By stripping away the friction of the how, AI frees geographers to focus entirely on the what.

When the mechanics of map-making are automated, the true value of a Geographer is revealed. Our expertise was never really about knowing which buttons to click in a software package; it was about understanding the world as a complex, interconnected spatial system.

In the AI era, our job shifts from being operators of software to being interrogators of data.

• What are the ethical implications of the spatial models we are building?
• What context is the AI missing about this local community?
• What are the systemic spatial inequalities—in health, climate vulnerability, or infrastructure—that we should be pointing these powerful new tools toward?

AI doesn’t know what questions are worth asking. It doesn’t possess geographical curiosity. It doesn’t understand the lived reality of a neighbourhood, the historical context of a border, or the nuances of human geography.

We are moving from a paradigm of spatial mechanics to one of spatial strategy. AI is finally allowing us to put down the digital spanners, look up from our screens, and focus our energy on what we are actually trying to solve. For anyone who got into geography to change the world rather than just digitise it, this is exactly where we want to be.

Next month’s blog post will be all about my other passion.. aviation photography, it’s the holiday season after all !