Some budget-friendly camera brands may supplement their income by analyzing user data or metadata to serve targeted ads or improve their AI models, often buried deep within a "Terms of Service" agreement that few people read. The "Neighborly" Privacy Gap
In many jurisdictions, you have a legal right to film public spaces (like the street) from your property, but filming areas where a neighbor has a "reasonable expectation of privacy" (like through their bedroom window) can lead to legal disputes or even harassment charges. How to Balance Security with Privacy
The primary privacy concern with modern security cameras is the vulnerability of the cloud. When you view your camera feed on your phone, that data is traveling through the internet.
Never use a security camera that doesn't offer 2FA. This ensures that even if a hacker gets your password, they can't access your cameras without a secondary code sent to your phone.
Be a good neighbor. Adjust your cameras to ensure they are focused on your entry points and property line, avoiding neighboring windows or private yards.
When your footage is stored on a company’s server, you aren’t the only one who has "access." There is a recurring debate regarding how much access law enforcement should have to private camera networks (such as Amazon’s Ring or Google’s Nest) without a warrant.
If privacy is your top priority, look for systems that support NVR (Network Video Recorder) or SD card storage . This keeps your footage on your own hardware, off the internet entirely.
Today’s systems are cloud-based and AI-driven. They use facial recognition to tell the difference between a family member and a stranger, infrared sensors to see in total darkness, and high-gain microphones to capture whispers. While these features make us safer, they also mean our most private moments—conversations in the kitchen, routines in the hallway—are being digitized, uploaded to servers, and processed by algorithms. The Risks: Data Breaches and "The Eye in the Cloud"
Some budget-friendly camera brands may supplement their income by analyzing user data or metadata to serve targeted ads or improve their AI models, often buried deep within a "Terms of Service" agreement that few people read. The "Neighborly" Privacy Gap
In many jurisdictions, you have a legal right to film public spaces (like the street) from your property, but filming areas where a neighbor has a "reasonable expectation of privacy" (like through their bedroom window) can lead to legal disputes or even harassment charges. How to Balance Security with Privacy
The primary privacy concern with modern security cameras is the vulnerability of the cloud. When you view your camera feed on your phone, that data is traveling through the internet.
Never use a security camera that doesn't offer 2FA. This ensures that even if a hacker gets your password, they can't access your cameras without a secondary code sent to your phone.
Be a good neighbor. Adjust your cameras to ensure they are focused on your entry points and property line, avoiding neighboring windows or private yards.
When your footage is stored on a company’s server, you aren’t the only one who has "access." There is a recurring debate regarding how much access law enforcement should have to private camera networks (such as Amazon’s Ring or Google’s Nest) without a warrant.
If privacy is your top priority, look for systems that support NVR (Network Video Recorder) or SD card storage . This keeps your footage on your own hardware, off the internet entirely.
Today’s systems are cloud-based and AI-driven. They use facial recognition to tell the difference between a family member and a stranger, infrared sensors to see in total darkness, and high-gain microphones to capture whispers. While these features make us safer, they also mean our most private moments—conversations in the kitchen, routines in the hallway—are being digitized, uploaded to servers, and processed by algorithms. The Risks: Data Breaches and "The Eye in the Cloud"
Data Dictionary: USDA National Agricultural Statistics Service, Cropland Data Layer
Source: USDA National Agricultural Statistics Service
The following is a cross reference list of the categorization codes and land covers.
Note that not all land cover categories listed below will appear in an individual state.
Raster
Attribute Domain Values and Definitions: NO DATA, BACKGROUND 0
Categorization Code Land Cover
"0" Background
Raster
Attribute Domain Values and Definitions: CROPS 1-60
Categorization Code Land Cover
"1" Corn
"2" Cotton
"3" Rice
"4" Sorghum
"5" Soybeans
"6" Sunflower
"10" Peanuts
"11" Tobacco
"12" Sweet Corn
"13" Pop or Orn Corn
"14" Mint
"21" Barley
"22" Durum Wheat
"23" Spring Wheat
"24" Winter Wheat
"25" Other Small Grains
"26" Dbl Crop WinWht/Soybeans
"27" Rye
"28" Oats
"29" Millet
"30" Speltz
"31" Canola
"32" Flaxseed
"33" Safflower
"34" Rape Seed
"35" Mustard
"36" Alfalfa
"37" Other Hay/Non Alfalfa
"38" Camelina
"39" Buckwheat
"41" Sugarbeets
"42" Dry Beans
"43" Potatoes
"44" Other Crops
"45" Sugarcane
"46" Sweet Potatoes
"47" Misc Vegs & Fruits
"48" Watermelons
"49" Onions
"50" Cucumbers
"51" Chick Peas
"52" Lentils
"53" Peas
"54" Tomatoes
"55" Caneberries
"56" Hops
"57" Herbs
"58" Clover/Wildflowers
"59" Sod/Grass Seed
"60" Switchgrass
Raster
Attribute Domain Values and Definitions: NON-CROP 61-65
Categorization Code Land Cover
"61" Fallow/Idle Cropland
"62" Pasture/Grass
"63" Forest
"64" Shrubland
"65" Barren
Raster
Attribute Domain Values and Definitions: CROPS 66-80
Categorization Code Land Cover
"66" Cherries
"67" Peaches
"68" Apples
"69" Grapes
"70" Christmas Trees
"71" Other Tree Crops
"72" Citrus
"74" Pecans
"75" Almonds
"76" Walnuts
"77" Pears
Raster
Attribute Domain Values and Definitions: OTHER 81-109
Categorization Code Land Cover
"81" Clouds/No Data
"82" Developed
"83" Water
"87" Wetlands
"88" Nonag/Undefined
"92" Aquaculture
Raster
Attribute Domain Values and Definitions: NLCD-DERIVED CLASSES 110-195
Categorization Code Land Cover
"111" Open Water
"112" Perennial Ice/Snow
"121" Developed/Open Space
"122" Developed/Low Intensity
"123" Developed/Med Intensity
"124" Developed/High Intensity
"131" Barren
"141" Deciduous Forest
"142" Evergreen Forest
"143" Mixed Forest
"152" Shrubland
"176" Grassland/Pasture
"190" Woody Wetlands
"195" Herbaceous Wetlands
Raster
Attribute Domain Values and Definitions: CROPS 195-255
Categorization Code Land Cover
"204" Pistachios
"205" Triticale
"206" Carrots
"207" Asparagus
"208" Garlic
"209" Cantaloupes
"210" Prunes
"211" Olives
"212" Oranges
"213" Honeydew Melons
"214" Broccoli
"215" Avocados
"216" Peppers
"217" Pomegranates
"218" Nectarines
"219" Greens
"220" Plums
"221" Strawberries
"222" Squash
"223" Apricots
"224" Vetch
"225" Dbl Crop WinWht/Corn
"226" Dbl Crop Oats/Corn
"227" Lettuce
"228" Dbl Crop Triticale/Corn
"229" Pumpkins
"230" Dbl Crop Lettuce/Durum Wht
"231" Dbl Crop Lettuce/Cantaloupe
"232" Dbl Crop Lettuce/Cotton
"233" Dbl Crop Lettuce/Barley
"234" Dbl Crop Durum Wht/Sorghum
"235" Dbl Crop Barley/Sorghum
"236" Dbl Crop WinWht/Sorghum
"237" Dbl Crop Barley/Corn
"238" Dbl Crop WinWht/Cotton
"239" Dbl Crop Soybeans/Cotton
"240" Dbl Crop Soybeans/Oats
"241" Dbl Crop Corn/Soybeans
"242" Blueberries
"243" Cabbage
"244" Cauliflower
"245" Celery
"246" Radishes
"247" Turnips
"248" Eggplants
"249" Gourds
"250" Cranberries
"254" Dbl Crop Barley/Soybeans