OterLabb

[    0.000000] Booting Linux on physical CPU 0x0000000000 [0x410fd034]
[    0.000000] Linux version 5.10.92-v8+ (dom@buildbot) (aarch64-linux-gnu-gcc-8 (Ubuntu/Linaro 8.4.0-3ubuntu1) 8.4.0, GNU ld (GNU Binutils for Ubuntu) 2.34) #1514 SMP PREEMPT Mon Jan 17 17:39:38 GMT 2022
[    0.000000] random: fast init done
[    0.000000] Machine model: Raspberry Pi Zero 2 W Rev 1.0
[    0.000000] efi: UEFI not found.
[    0.000000] Reserved memory: created CMA memory pool at 0x000000000bc00000, size 256 MiB
[    0.000000] OF: reserved mem: initialized node linux,cma, compatible id shared-dma-pool
[    0.000000] Zone ranges:
[    0.000000]   DMA      [mem 0x0000000000000000-0x000000001bffffff]
[    0.000000]   DMA32    empty

OterLabb

// GEE Script for å hente ut NDVI og SDVI verdier fra punkter
// Eksporterer til .csv fil
// Scriptet må kjøres to ganger, første gangen for å finne antall satelittbilder,
// så skrive inn antallet bilder på linje 70
// Polygon (studie området) heter 'stud_omraade'¨
// Random points heter 'table'

// Importere verktøy for å filtere ut skyer/vann/skygger
// Taken from André Hollstein et al. 2016 (doi:10.3390/rs8080666)
// http://www.mdpi.com/2072-4292/8/8/666/pdf

OterLabb

library(raster)
library(dismo)
library(usdm)

raster_data <- raster("C:/MinData/satelittdata.tif")
kraake_points <- read.csv("C:/MinData/kraakepoints.csv")

# Remove duplicates (same XY coordinates)
kraake_points <- kraake_points[!duplicated(kraake_points[c("Breddegrad","Lengdegrad")]),]

OterLabb

library(stringr)

grav_roys <- read.csv("G:/New Folder (18)/rays_haug.csv", encoding = "UTF-8", sep = ",")
str(grav_roys)
myvars <- c("OBJECTID", "objid", "informasjo")
newdata <- grav_roys[myvars]

subset <- head(grav_roys)
subset
str(subset)

OterLabb

import arcpy, os  
  
def getLayerOnName(mxd, lyr_name):  
    for lyr in arcpy.mapping.ListLayers(mxd):  
        if lyr.name.upper() == lyr_name.upper():  
            return lyr  
            break  
  
# some settings and variables  
mxd_path = "CURRENT" # or use a path to the mxd file  

OterLabb

from qgis.core import QgsRasterLayer
from PyQt5.QtCore import QFileInfo
import os

maindir = r'G:\kulturminner\New Folder (18)\501ad5c3\slope2'
files = [x for x in os.listdir(maindir) if x.endswith(".tif")]
outFolder = os.path.join(maindir, 'rendered')

def StringToRaster(file, filepath):
    # Check if string is provided

OterLabb

import arcpy  
arcpy.CheckOutExtension("Spatial")  
from arcpy import env
from arcpy.sa import *  
import os

maindir = r'G:\RasterFolder'
files = [x for x in os.listdir(maindir) if x.endswith(".tiff")]
outFolder = os.path.join(maindir, 'slope')

OterLabb

import arcpy
import time			
			
mxd = arcpy.mapping.MapDocument('CURRENT')
df = arcpy.mapping.ListDataFrames(mxd, "Layers") [0]
lyr = arcpy.mapping.ListLayers(mxd, "Layers", df)[0]			
lst_shapes = [row[0] for row in arcpy.da.SearchCursor('feature_class', ['SHAPE@'])]  

for shape in lst_shapes:   
    df.extent = shape.extent  

OterLabb

import json
import os
import sys

import numpy as np

sys.path.append(os.path.dirname(__file__))
import importlib
from skimage.measure import find_contours
import keras.backend as K

OterLabb

Messages
Start Time: 05 March 2019 01:17:35
Distributing operation across 4 parallel instances.
 ERROR 999999: Something unexpected caused the tool to fail. Contact Esri Technical Support (http://esriurl.com/support) to Report a Bug, and refer to the error help for potential solutions or workarounds.
 Python raster function is unable to vectorize the data.
 Traceback (most recent call last):
 File "c:\program files\arcgis\pro\Resources\Raster\Functions\System\DeepLearning\ObjectDetector.py", line 101, in vectorize
 polygon_list, scores, classes = self.child_object_detector.vectorize(**pixelBlocks)
 File "c:\program files\arcgis\pro\Resources\Raster\Functions\System\DeepLearning\Keras\MaskRCNN.py", line 96, in vectorize
 results = self.model.detect([image], verbose=1)