Flight tracking data is an important tool in open source research, but with 100,000 daily flights, it can be difficult to contextualise what a particular aircraft’s movements indicate. 

Bellingcat has developed a tool called Turnstone to make it easier to visualise historical trends in flight data and spot unusual patterns. It also allows users to filter by parameters such as aircraft type or a geographic region of interest. 

This tool primarily uses Automatic Dependent Surveillance–Broadcast (ADS-B) data, the technology that enables open source investigators and enthusiasts to track flights. 

Most aircraft are equipped with transmitters that broadcast ADS-B data to comply with global aviation regulations, though regulations vary by jurisdiction, and military aircraft might not always transmit. ADS-B data includes information about an aircraft’s identity and type, as well as its precise position, speed and altitude. 

Popular flight-tracking websites such as Flightradar24 and ADS-B Exchange typically display historical data for a particular time or aircraft. However, Turnstone aggregates ADS-B data for multiple aircraft over time, and allows users to search for flights across two areas of interest at once. These features provide additional context for open source investigators to better understand flight behaviour.

Watch the video for a demonstration of how the tool works, using the example of Black Hawk helicopter patrols near one of the borders between the US and Canada:

You can view Turnstone’s source code and information about hosting it yourself on Bellingcat’s GitHub. 

We also have a web-based instance of the tool that journalists and academics can access. Due to data hosting and processing costs, we can only grant access on a selective basis. If you would like to apply, please fill in this form. Priority will be given to researchers conducting open source investigations aligned with Bellingcat’s goals.

Read on for more examples of how Turnstone can be used for investigations, as well as some limitations of the tool.  

Spotting Unusually High US Tanker Activity Before Iran Strikes

The US and Israel launched joint air strikes across Iran on Feb. 28, 2026, reportedly killing more than 1,000 people, including members of the Iranian leadership, in five days.

This marked a dramatic escalation since the US and Israel bombed three Iranian nuclear sites in June 2025. 

Flight data before both the June 2025 and February 2026 strikes showed a large number of American aerial tankers leaving the US and crossing the Atlantic towards Iran. Aerial tankers such as the KC-135 and KC-46A can refuel military aircraft in-flight, making them essential for most long-range combat missions.

The 9 KC-46As that went to Ben Gurion.

All came direct from the eastern U.S. pic.twitter.com/izANyrqi4Q

— Evergreen Intel (@vcdgf555) February 27, 2026

With Turnstone, it is possible to interrogate the baseline level of movement and see how unusual this activity is.

To do this, three filters are set on the search: a geographic region of interest, set to the North Atlantic, a filter on the aircraft type, to search only for tankers, and a filter on the aircraft heading, to search only for eastbound traffic.

[Note: For the aircraft category designations, Bellingcat used a custom-prompted large language model (LLM), Claude Sonnet 4.0, to assign a category label using aircraft type code data. There may be some inaccuracies in the classifications, as LLMs are prone to hallucinations. We discuss this further in the “Limitations of the Data” section of this piece.]

This search finds over 40,000 aircraft locations that match these filter queries. However, a look at the summary table shows that this data includes non-American tankers as well.

We can filter this data to include only aircraft associated with the US by typing “United States” into the search box in the table. Note that ownership data is not 100 percent accurate – it may be out of date, especially for privately owned aircraft, and new aircraft might not have any data at all. However, especially when comparing trends over time or searching for research leads, this data can still be useful.

The graph of matching detections over time now shows that while there is a large baseline level of transatlantic movement for American tankers, there was a notably higher number of American tankers heading eastward from the US across the North Atlantic detected in the week of June 15, 2025, as well as in the last two weeks of February 2026.

A week after the increased eastbound traffic in June 2025, early in the morning on June 22, the US struck several nuclear sites in Iran. And on Feb. 28, 2026, the US and Israel launched over 900 strikes against Iran.

Altering the search query to look for westbound tankers instead of eastbound tankers, we can also see a larger-than-normal number of American tankers heading in the direction of the US during the week of July 13, 2025, bookending the summer airstrikes in Iran. No such return movement is yet visible following the recent strikes.

Finding Deportation Flights to Guantanamo Bay

Turnstone also allows you to search for aircraft detected across two different geographic regions of interest (ROIs). 

Shortly after US President Donald Trump announced the opening of a migrant detention centre at Guantanamo Bay in Cuba at the end of January 2025, the US military reportedly flew about 100 immigrants from El Paso, Texas, to the US naval base to await deportation. By selecting the areas around both Guantanamo Bay and El Paso, we can find flights between these cities that broadcast ADS-B data.

When two ROIs are selected, you can also enter the maximum time difference between an aircraft’s presence in the two regions. 

In the example below, we have entered 36,000 seconds (10 hours), meaning that the aircraft must have crossed through both regions within 10 hours of each other. We have also set the maximum altitude to 15,000 ft (4.57km) to look for planes landing and taking off. This limit is set relatively high as there are no ADS-B receivers at Guantanamo Bay, and only the initial approach is captured.

After five months with no tracked flights between the two locations, this search shows an uptick in flights in the few months from February 2025.

Results for this search query from Jan. 26, 2026, include several passenger aircraft operated by companies known to run deportation flights from the US, such as Omni Air International and Global Crossing Airlines.

Mapping US Customs and Border Patrol Aircraft

Turnstone also supports uploading a list of International Civil Aviation Organization (ICAO) addresses, informally referred to as aircraft “hex codes”, which are unique identifiers assigned to aircraft by ICAO member states.

For example, to explore data related to Department of Homeland Security (DHS) activity and look for patterns related to the US immigration enforcement and border security operations, we can copy and paste the hex codes from a list of US Customs and Border Patrol (CBP) aircraft (used across the DHS) into a text file, and upload that file. Now, we can search among these aircraft with any of the same filters demonstrated in the earlier case studies. Alternatively, we can also deselect all of the filters to track the most recent activity by those aircraft.

Let’s try that with the CBP list, this time with a very large number of results selected: 500,000. Note that increasing the number of results increases the search time and requires more browser memory.

When many points are displayed, the map is simplified, and hover features are disabled.

By the California-Mexico border, Eurocopter AS350 (type “AS50”) can be seen on frequent patrol missions over the land border. Over the Pacific Ocean, Black Hawk helicopters (“H60”) can be seen patrolling the international waters boundary off the Mexican coast, while CBP Dash-8s (“DH8B” and “DH8C”) travel farther offshore.

In contrast, by the Minnesota-Canada border, CBP makes more active use of one of its MQ-9 Reaper drones, as seen from the prevalence of red dots that correspond to “Q9”, the type code of these drones, in the results map.

Let’s take a closer look at these drones by filtering the results with the text “Q9”. Now the displayed aircraft only include MQ-9 Reaper drones.

Now we can take a closer look at the patterns of drones, specifically among the search results.

While overall CBP flight activity was relatively stable, drone flights seem to have intensified in December 2025 and January 2026, compared with previous weeks.

Limitations of the Data

In open source research, it is always important to be alert to the limitations of a particular data source, and ADS-B data is no exception. 

For example, some aircraft do not have ADS-B transponders and use older transponders to transmit flight information, which can result in tracking tools such as Turnstone showing inaccurate position data. 

In the previous case study of CBP aircraft, the Turnstone results appeared to show an MQ-9 Reaper drone in Canada on Jan. 20, 2026. 

Is this evidence of covert DHS missions in Canadian airspace? Likely not: a cross-check of the drone’s hex code on that date with ADS-B Exchange shows that the aircraft’s position track is not smooth, but jumps back and forth between a line in the US and several points many kilometres away in Canada.

This happens because when ADS-B position data is not available, flight trackers often use multilateration (MLAT), which estimates the location of the aircraft using the time differences between signals transmitted from known sites, as a substitute. The flight tracking information on ADS-B Exchange shows that the position was calculated using MLAT, which is less accurate than position data directly transmitted through ADS-B. ADSB.lol, which is the data source used by Turnstone, uses MLAT when ADS-B position data is not available.  

ADS-B data is also limited by where ground antennas are available to receive radio signals from aircraft and by when aircraft choose to transmit the data.

Other datasets which Bellingcat has used to enable the filters available on Turnstone each have their own limitations. 

There is no single source of data on aircraft ownership. ADS-B data identifies an aircraft only using its ICAO address or hex codes, but does not contain other information that directly specifies the type of aircraft or its registration.

Instead, flight-tracking websites reference aircraft registration databases, such as those maintained by the US Federal Aviation Administration, to correlate ICAO addresses with registration information. The ownership data displayed on Turnstone is from tar1090-db, a community-maintained project which has produced the most comprehensive freely available global aircraft registration database. However, since ownership data is collected from many jurisdictions, with different privacy and disclosure requirements, it may sometimes be out-of-date or misleading. 

Ownership information displayed in Turnstone or any other flight-tracking software should still be verified independently using multiple sources.

For example, one of the aircraft that came up in the search for flights between El Paso and Guantanamo Bay had a hex code of a6b0f5. This showed up in Turnstone’s results as being owned by Bank of Utah Trustee, which matches the operator listed for this flight on ADS-B Exchange. But some of the flight codes used by this aircraft, starting with “GXA”, are used by Global Crossing Airlines (GlobalX). The Bank of Utah is known to legally own aircraft under a trust relationship, while leasing the aircraft and operational control to third parties such as GlobalX.

The “Category” label and “Military” flag, which provide a convenient way to filter aircraft, are pre-generated by a custom-prompted large language model, Claude Sonnet 4.0, based on the make and model of an aircraft. 

For example, the LLM may take a type code of A321, which refers to an Airbus A321 passenger jet, as input and assign the corresponding aircraft the category of “airliner”. 

Bellingcat manually verified over 80 per cent of aircraft, corresponding to the most common aircraft types. But as we know, LLMs are prone to hallucinations, and categorisation may be inaccurate for more obscure aircraft. Additionally, some aircraft, such as the V-22 Osprey, fall between categories and are inherently ambiguous. 

To prevent errors caused by the potential miscategorisation of aircraft, you may want to search by type code, which will draw from the raw tar1090-db data, rather than category. All aircraft registration, type, and owner information should be independently verified.

Suggestions and Further Information

As we’ve seen in this guide, Turnstone searches historical ADS-B data to allow researchers to explore flight patterns over time and in specific locations. While flight-tracking data has inherent limitations, Turnstone can provide useful leads for researchers looking to incorporate flight tracking in their investigations.

If you have suggestions for improving the tool, you can submit a pull request on Bellingcat’s GitHub. More technical information can also be found in the tool’s README.

For more demos and information about the history of this tool, watch a talk that Bellingcat gave about it at the What Hackers Yearn (WHY) 2025 hacker camp:

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Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here and Mastodon here.

The post Using Bellingcat’s New Open Source Tool to Explore Historical and Spatial Flight Data appeared first on bellingcat.

Since the beginning of 2023, big tech has spent over $814 billion in capital expenditures, with a large portion of that going towards meeting the demands of AI companies like OpenAI and Anthropic. 

Big tech has spent big on GPUs, power infrastructure, and data center construction,  using a variety of financing methods to do so, including (but not limited to) leasing. And the way they’re going about structuring these finance deals is growing increasingly bizarre. 

I’m not merely talking about Meta’s curious arrangement for its facility in Louisiana, though that certainly raised some eyebrows. Last year, Morgan Stanley published a report that claimed hyperscalers were increasingly relying on finance leases to obtain the “powered shell” of a data center, rather than the more common method of operating leases. 

The key difference here is that finance leases, unlike operating leases, are effectively long-term loans where the borrower is expected to retain ownership of the asset (whether that be a GPU or a building) at the end of the contract. Traditionally, these types of arrangements have been used to finance the bits of a data center that have a comparatively limited useful life — like computer hardware, which grows obsolete with time.

The spending to date is, as I’ve written about again and again, an astronomical amount of spending considering the lack of meaningful revenue from generative AI. 

Even after a year straight of manufacturing consent for Claude Code as the be-all-end-all of software development resulted in putrid results for Anthropic — $4.5 billion of revenue and $5.2 billion of losses before interest, taxes, depreciation and amortization according to The Information — with (per WIRED) Claude Code only accounting for around $1.1 billion in annualized revenue in December, or around $92 million in monthly revenue.

This was in a year where Anthropic raised a total of $16.5 billion (with $13 billion of that coming in September 2025), and it’s already working on raising another $25 billion. This might be because it promised to buy $21 billion of Google TPUs from Broadcom, or because Anthropic expects AI model training costs to cost over $100 billion in the next 3 years. And it just raised another $30 billion — albeit with the caveat that some of said $30 billion came from previously-announced funding agreements with Nvidia and Microsoft, though how much remains a mystery.

According to Anthropic’s new funding announcement, Claude Code’s run rate has grown to “over $2.5 billion” as of February 12 2026 — or around $208 million. Based on literally every bit of reporting about Anthropic, costs have likely spiked along with revenue, which hit $14 billion annualized ($1.16 billion in a month) as of that date. 

I have my doubts, but let’s put them aside for now.

Anthropic is also in the midst of one of the most aggressive and dishonest public relations campaigns in history. While its Chief Commercial Officer Paul Smith told CNBC that it was “focused on growing revenue” rather than “spending money,” it’s currently making massive promises — tens of billions on Google Cloud, “$50 billion in American AI infrastructure,” and $30 billion on Azure. And despite Smith saying that Anthropic was less interested in “flashy headlines,” Chief Executive Dario Amodei has said, in the last three weeks, that “almost unimaginable power is potentially imminent,” that AI could replace all software engineers in the next 6-12 months, that AI may (it’s always fucking may) cause “unusually painful disruption to jobs,” and wrote a 19,000 word essay — I guess AI is coming for my job after all! — where he repeated his noxious line that “we will likely get a century of scientific and economic progress compressed in a decade.”

Training Costs Should Be Part of AI Labs’ Gross Margins, And To Not Include Them Is Deceptive

Yet arguably the most dishonest part is this word “training.” When you read “training,” you’re meant to think “oh, it’s training for something, this is an R&D cost,” when “training LLMs” is as consistent a cost as inference (the creation of the output) or any other kind of maintenance. 

While most people know about pretraining — the shoving of large amounts of data into a model (this is a simplification I realize) — in reality a lot of the current spate of models use post-training, which covers everything from small tweaks to model behavior to full-blown reinforcement learning where experts reward or punish particular responses to prompts.

To be clear, all of this is well-known and documented, but the nomenclature of “training” suggests that it might stop one day, versus the truth: training costs are increasing dramatically, and “training” covers anything from training new models to bug fixes on existing ones. And, more fundamentally, it’s an ongoing cost — something that’s an essential and unavoidable cost of doing business. 

Training is, for an AI lab like OpenAI and Anthropic, as common (and necessary) a cost as those associated with creating outputs (inference), yet it’s kept entirely out of gross margins:

Anthropic has previously projected gross margins above 70% by 2027, and OpenAI has projected gross margins of at least 70% by 2029, which would put them closer to the gross margins of publicly traded software and cloud firms. But both AI developers also spend a tremendous amount on renting servers to develop new models—training costs, which don’t factor into gross margins—making it more difficult to turn a net profit than it is for traditional software firms.

This is inherently deceptive. While one would argue that R&D is not considered in gross margins, training isn’t gross margins — yet gross margins generally include the raw materials necessary to build something, and training is absolutely part of the raw costs of running an AI model. Direct labor and parts are considered part of the calculation of gross margin, and spending on training — both the data and the process of training itself — are absolutely meaningful, and to leave them out is an act of deception. 

Anthropic’s 2025 gross margins were 40% — or 38% if you include free users of Claude — on inference costs of $2.7 (or $2.79) billion, with training costs of around $4.1 billion. What happens if you add training costs into the equation? 

Let’s work it out!

• If Anthropic’s gross margin was 38% in 2025, that means its COGS (cost of goods sold) was $2.79 billion.
• If we add training, this brings COGS to $6.89 billion, leaving us with -$2.39 billion after $4.5 billion in revenue.
• This results in a negative 53% gross margin.

Training is not an up front cost, and considering it one only serves to help Anthropic cover for its wretched business model. Anthropic (like OpenAI) can never stop training, ever, and to pretend otherwise is misleading. This is not the cost just to “train new models” but to maintain current ones, build new products around them, and many other things that are direct, impossible-to-avoid components of COGS. They’re manufacturing costs, plain and simple.

Anthropic projects to spend $100 billion on training in the next three years, which suggests it will spend — proportional to its current costs — around $32 billion on inference in the same period, on top of $21 billion of TPU purchases, on top of $30 billion on Azure (I assume in that period?), on top of “tens of billions” on Google Cloud. When you actually add these numbers together (assuming “tens of billions” is $15 billion), that’s $200 billion. 

Anthropic (per The Information’s reporting) tells investors it will make $18 billion in revenue in 2026 and $55 billion in 2027 — year-over-year increases of 400% and 305% respectively, and is already raising $25 billion after having just closed a $30bn deal. How does Anthropic pay its bills? Why does outlet after outlet print these fantastical numbers without doing the maths of “how does Anthropic actually get all this money?”

Because even with their ridiculous revenue projections, this company is still burning cash, and when you start to actually do the maths around anything in the AI industry, things become genuinely worrying. 

You see, every single generative AI company is unprofitable, and appears to be getting less profitable over time. Both The Information and Wall Street Journal reported the same bizarre statement in November — that Anthropic would “turn a profit more quickly than OpenAI,” with The Information saying Anthropic would be cash flow positive in 2027 and the Journal putting the date at 2028, only for The Information to report in January that 2028 was the more-realistic figure. 

If you’re wondering how, the answer is “Anthropic will magically become cash flow positive in 2028”:

This is also the exact same logic as OpenAI, which will, per The Information in September, also, somehow, magically turn cashflow positive in 2030:

Oracle, which has a 5-year-long, $300 billion compute deal with OpenAI that it lacks the capacity to serve and that OpenAI lacks the cash to pay for, also appears to have the same magical plan to become cash flow positive in 2029:

Somehow, Oracle’s case is the most legit, in that theoretically at that time it would be done, I assume, paying the $38 billion it’s raising for Stargate Shackelford and Wisconsin, but said assumption also hinges on the idea that OpenAI finds $300 billion somehow.

it also relies upon Oracle raising more debt than it currently has — which, even before the AI hype cycle swept over the company, was a lot. 

As I discussed a few weeks ago in the Hater’s Guide To Oracle, a megawatt of data center IT load generally costs  (per Jerome Darling of TD Cowen) around $12-14m  in construction (likely more due to skilled labor shortages, supply constraints and rising equipment prices) and $30m a megawatt in GPUs and associated hardware. In plain terms, Oracle (and its associated partners) need around $189 billion to build the 4.5GW of Stargate capacity to make the revenue from the OpenAI deal, meaning that it needs around another $100 billion once it raises $50 billion in combined debt, bonds, and printing new shares by the end of 2026.

I will admit I feel a little crazy writing this all out, because it’s somehow a fringe belief to do the very basic maths and say “hey, Oracle doesn’t have the capacity and OpenAI doesn’t have the money.” In fact, nobody seems to want to really talk about the cost of AI, because it’s much easier to say “I’m not a numbers person” or “they’ll work it out.”

This is why in today’s newsletter I am going to lay out the stark reality of the AI bubble, and debut a model I’ve created to measure the actual, real costs of an AI data center.

While my methodology is complex, my conclusions are simple: running AI data centers is, even when you remove the debt required to stand up these data centers, a mediocre business that is vulnerable to basically any change in circumstances. 

Based on hours of discussions with data center professionals, analysts and economists, I have calculated that in most cases, the average AI data center has gross margins of somewhere between 30% and 40% — margins that decay rapidly for every day, week, or month that you take putting a data center into operation.

This is why Oracle has negative 100% margins on NVIDIA’s GB200 chips — because the burdensome up-front cost of building AI data centers (as GPUs, servers, and other associated) leaves you billions of dollars in the hole before you even start serving compute, after which you’re left to contend with taxes, depreciation, financing, and the cost of actually powering the hardware. 

Yet things sour further when you face the actual financial realities of these deals — and the debt associated with them. 

Based on my current model of the 1GW Stargate Abilene data center, Oracle likely plans to make around $11 billion in revenue a year from the 1.2GW (or around 880MW of critical IT). While that sounds good, when you add things like depreciation, electricity, colocation costs of $1 billion a year from Crusoe, opex, and the myriad of other costs, its margins sit at a stinkerific 27.2% — and that’s assuming OpenAI actually pays, on time, in a reliable way.

Things only get worse when you factor in the cost of debt. While Oracle has funded Abilene using a mixture of bonds and existing cashflow, it very clearly has yet to receive the majority of the $25 billion+ in GPUs and associated hardware (with only 96,000 GPUs “delivered”), meaning that it likely bought them out of its $18 billion bond sale from last September. 

If we assume that maths, this means that Oracle is paying a little less than $963 million a year (per the terms of the bond sale) whether or not a single GPU is even turned on, leaving us with a net margin of 22.19%... and this is assuming OpenAI pays every single bill, every single time, and there are absolutely no delays.

These delays are also very, very expensive. Based on my model, if we assume that 100MW of critical IT load is operational (roughly two buildings and 100,000 GB200s) but has yet to start generating revenue, Oracle is burning, without depreciation (EDITOR’S NOTE: sorry! This previously said depreciation was a cash expense and was included in this number (even though it wasn’t!), but it's correct in the model!), around $4.69 million a day in cash. I have also confirmed with sources in Abilene that there is no chance that Stargate Abilene is fully operational in 2026.

In simpler terms:

• AI startups are all unprofitable, and do not appear to have a path to sustainability. 
• AI data centers are being built in anticipation of demand that doesn’t exist, and will only exist if AI startups — which are all unprofitable — can afford to pay them.
• Oracle, which has committed to building 4.5GW of data centers, is burning cash every day that OpenAI takes to set up its GPUs, and when it starts making money, it does so from a starting position of billions and billions of dollars in debt.
• Margins are low throughout the entire stack of AI data center operators — from landlords like Applied Digital to compute providers like CoreWeave — thanks to the billions in debt necessary to fund both construction and IT hardware to make them run, putting both parties in a hole that can only be filled with revenues that come from either hyperscalers or AI startups. 
• In a very real sense, the AI compute industry is dependent on AI “working out,” because if it doesn’t, every single one of these data centers will become a burning hole in the ground.

I will admit I’m quite disappointed that the media at large has mostly ignored this story. Limp, cautious “are we in an AI bubble?” conversations are insufficient to deal with the potential for collapse we’re facing. 

Today, I’m going to dig into the reality of the costs of AI, and explain in gruesome detail exactly how easily these data centers can rapidly approach insolvency in the event that their tenants fail to pay. 

The chain of pain is real:

These GPUs are purchased, for the most part, using debt provided by banks or financial institutions. While hyperscalers can and do fund GPUs using cashflow, even they have started to turn to debt.

At that point, the company that bought the GPUs sinks hundreds of millions of dollars to build a data center, and once it turns on, provides compute to a model provider, which then begins losing money selling access to those GPUs. For example, both OpenAI and Anthropic lose billions of dollars, and both rely on venture capital to fund their ability to continue paying for accessing those GPUs.

At that point, OpenAI and Anthropic offer either subscriptions — which cost far more to offer than the revenue they provide — or API access to their models on a per-million-token basis. AI startups pay to access these models to run their services, which end up costing more than the revenue they make, which means they have to raise venture capital to continue paying to access those models.

Outside of hyperscalers paying NVIDIA for GPUs out of cashflow, none of the AI industry is fueled by revenue. Every single part of the industry is fueled by some kind of subsidy.

As a result, the AI bubble is really a stress test of the global venture capital, private equity, private credit, institutional and banking system, and its willingness to fund all of this forever, because there isn't a single generative AI company that's got a path to profitability.

Today I’m going to explain how easily it breaks.

Privacy-focused email provider Proton Mail provided Swiss authorities with payment data that the FBI then used to determine who was allegedly behind an anonymous account affiliated with the Stop Cop City movement in Atlanta, according to a court record reviewed by 404 Media.

The records provide insight into the sort of data that Proton Mail, which prides itself both on its end-to-end encryption and that it is only governed by Swiss privacy law, can and does provide to third parties. In this case, the Proton Mail account was affiliated with the Defend the Atlanta Forest (DTAF) group and Stop Cop City movement in Atlanta, which authorities were investigating for their connection to arson, vandalism and doxing. Broadly, members were protesting the building of a large police training center next to the Intrenchment Creek Park in Atlanta, and actions also included camping in the forest and lawsuits. Charges against more than 60 people have since been dropped. 

Just a two hour’s drive from the ever-bustling and expanding sprawl of Mumbai—India’s second city—the Sahyadris Hills unfold with a cooler and less humid climate. For decades now, the region—also noted for its ancient forts and Buddhist caves—has been a popular weekend destination for city-dwellers in search of respite but, like the dense and crowded urban cores they’re escaping, it has also succumbed to rampant and unregulated development. Ironically, much of the natural beauty here has fallen victim to its fame. Such is the unfortunate fate of many sought-after resort areas in favorable proximity to a major city or conurbation.

Looking to maintain what is left of the verdant landscape and make it available to guests in a more responsible way is the recently completed Radisson Resort and Spa Lonavala.

Replete with several distinctive restaurants, events venues, and other premium amenities, the retreat embeds into its ‘hill station’ surroundings through its innovative architectural massing, materiality, and the implementation of climate-specific strategies inspired by the local architectural vernacular. The decidedly Brutalist yet emphatically site-responsive complex is a far cry from the antiseptic chain hotels that often feel out-of-place and inauthentic.

“The sense of open space of connecting to nature is contested by the building forms that emerge through the prescribed structural codes and densities,” says Kamal Malik, the founder the eponymously named, Mumbai-based firm responsible for the hotel’s design “The architecture emerges from the site, topography, from the region’s material history—black basalt and wood—and adapts to both flexible and fixed—public and private—programs.”

Malik Architecture’s comprehensive intervention incorporates reinterpreted architectural archetypes from the region: courtyards, verandahs, deep-shading, thick walls, and cross-ventilation. The main focus however is the surrounding nature. Monumental volumes give way to soaring apertures visible for numerous semi-indoor and semi-outdoor vantage points. All together, the scheme accommodates optimal natural ventilation, safeguards against heavy rain during Monsoon season, and protects against harsh sun rays.

Suites come with adjoining patios enclosed by operable slatted shading walls. The guest rooms occupy abstract-form buildings hovering above the ground. Shafts of natural light slice across these angular volumes and illuminate atriums that appear between.

“The feeling of a multi-storey building has been avoided by developing the ground as organic, free form public spaces with split level topographical connections,” Malik adds. “Extant forms—bastions and Large masonry walls—stepped courts, otherwise known as kunds—animate the built landscape.”

A rich earth tone palette, defined by many of the same substrate materials as the exterior, makes its way into the interiors but doesn’t overpower as the main attraction remains the carefully framed natural setting outside. There’s no superfluous decoration, just a sober deployment of ornamentation hinting at the local Maratha culture. Spacious guest rooms and suites are pared back with a calming modernist aesthetic only interrupted by fluted tambour-pattern feature walls and traditional carpets denoting the placement of beds.

Alongside numerous sports facilities including everything from a fully-equipped fitness center and steam room to archery and badminton fields, Radisson Resort and Spa Lonavala’s dining options include the quintessential Indian style Hirkani—with stations that allow guest to watch their food being prepared—and a series of pop-up haunts. Malhari is the go-to cocktail bar. The historic Tungarli Village situates right out the resort.

What: Radisson Resort and Spa Lonavala
Where: Lonavala, India
How much: Rooms starting at $137
Design draws: A site responsible destination embedded within the popular getaway Sahyadris region of western India with materiality, proportions, cuisine, and activities programmed in honor of the verdant nature in the immediate surroundings.

Go virtually on vacation with more design destinations right here.

Photography courtesy of Radisson Resort and Spa.

Simons ewig hungrige Katze findet einige Wege, um den Laptop seines Menschen zu zerstören, als Simon während einer Telefonkonferenz den Raum verlassen musste.

If you’ve ever tried to work with a cat in the room, this one’s for you. Working from home isn’t so peaceful with this keyboard warrior around!

(Direktlink, via Laughing Squid)