Photos | Urban Escape: Skating under San Francisco's Arches

Captured on 24th February 2024, Wesley finds serenity carving paths on his skateboard under the iconic bridges of Fort Point, San Francisco. Surrounded by city architecture, the rich textures of brick and path contrast with the denim-clad figure passing through an urban sanctuary. Amidst a blend of the manmade and the natural, this moment immortalizes the vibrancy and solitude found in the smallest corners of our urbane landscape.
BLIP-2 Description:
a person standing on a skateboard under a bridgeMetadata
Capture date:
Original Dimensions:
3024w x 4032h - (download 4k)
Usage
Dominant Color:
Location:
february path walkway machine shelter bird wheel randoms city outdoors urban slate fountain hole wesley car vehicle jeans sky construction plant tree transportation summer tire photography rock sidewalk corrosion outdoor street animal pants road arch window building footwear architecture flagstone spoke water portrait shorts rust standing factory basin sundial nature soil brick alloy shoe
iso
40
metering mode
5
aperture
f/2.2
focal length
2mm
latitude
37.81
longitude
-122.48
shutter speed
1/879s
camera make
Apple
camera model
date
2024-02-24T15:29:02.001000-08:00
tzoffset
-28800
tzname
GMT-0800
overall
(55.22%)
curation
(65.00%)
highlight visibility
(5.50%)
behavioral
(70.58%)
failure
(-0.15%)
harmonious color
(8.51%)
immersiveness
(3.47%)
interaction
(2.00%)
interesting subject
(23.62%)
intrusive object presence
(-6.45%)
lively color
(13.88%)
low light
(3.81%)
noise
(-3.34%)
pleasant camera tilt
(-2.13%)
pleasant composition
(6.23%)
pleasant lighting
(15.84%)
pleasant pattern
(9.89%)
pleasant perspective
(22.86%)
pleasant post processing
(3.21%)
pleasant reflection
(-0.36%)
pleasant symmetry
(1.42%)
sharply focused subject
(1.71%)
tastefully blurred
(3.32%)
well chosen subject
(-8.31%)
well framed subject
(28.25%)
well timed shot
(12.28%)
all
(11.43%)
* WARNING: The title and caption of this image were generated by an AI LLM (gpt-4-0613
from
OpenAI)
based on a
BLIP-2 image-to-text labeling, tags,
location,
people
and album metadata from the image and are
potentially inaccurate, often hilariously so. If you'd like me to adjust anything,
just reach out.