Photos | Vibrant Nightlife in Korea, 2024

Taken on a bustling city street in Korea during the late hours of March 20, 2024, this photograph encapsulates the urban vibrance of a metropolis at night. One can clearly see 14 individuals, each with their own unique style, crossing an intersection under the glow of colorful billboards and street signs. Between the rows of 9 parked cars and buzzing digital screens, dappled light from nearby buildings punctuates the scene, creating a visually striking balance between technology, urban living, and personal style.
BLIP-2 Description:
a city street at nightMetadata
Capture date:
Original Dimensions:
6000w x 4000h - (download 4k)
Usage
Dominant Color:
neighborhood urban building 다이아몬드 footwear metropolitan pedestrian outdoors transportation sidewalk hat area ga crosswalk road outdoor intersection crossing sky tarmac city shoe bag traffic land street 金 hardware jongno 한국표준 bicycle metropolis path bus 한국표준다 billboards sign electronics car vehicle screen zebra light accessories monitor downtown urban area korea computer handbag symbol
iso
4000
metering mode
5
aperture
f/3.2
focal length
24mm
shutter speed
1/200s
camera make
Canon
camera model
lens model
date
2024-03-20T03:11:17-07:00
tzoffset
-25200
tzname
America/Los_Angeles
overall
(44.56%)
curation
(50.00%)
highlight visibility
(4.51%)
behavioral
(90.72%)
failure
(-0.54%)
harmonious color
(1.39%)
immersiveness
(1.46%)
interaction
(1.00%)
interesting subject
(-36.89%)
intrusive object presence
(-6.01%)
lively color
(-12.49%)
low light
(99.12%)
noise
(-1.88%)
pleasant camera tilt
(-10.73%)
pleasant composition
(-55.27%)
pleasant lighting
(-30.32%)
pleasant pattern
(4.42%)
pleasant perspective
(6.89%)
pleasant post processing
(-0.74%)
pleasant reflection
(-0.69%)
pleasant symmetry
(1.59%)
sharply focused subject
(0.27%)
tastefully blurred
(-6.43%)
well chosen subject
(1.76%)
well framed subject
(-34.77%)
well timed shot
(-0.82%)
all
(-2.39%)
* 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.